Downloads & Free Reading Options - Results

Recursive frame analysis (RFA), both a practical therapeutic tool and an advanced qualitative research method that maps the structure of therapeutic conversation, is introduced with a clinical case vignette. We present and illustrate a means of mapping metaphorical themes that contextualize the performance taking place in the room, recursively enacted to produce a lineal progression from an opening act to a closing act. RFA is offered to therapists, supervisors, teachers, and researchers as an exit from impoverished ways of framing both the choices we have in how to work with clients as well as the ways in which pedagogy is structured and research conducted. (Contains 5 figures and 8 footnotes.)

In aspect-based sentiment analysis, extracting aspect terms along with the opinions being expressed from user-generated content is one of the most important subtasks. Previous studies have shown that exploiting connections between aspect and opinion terms is promising for this task. In this paper, we propose a novel joint model that integrates recursive neural networks and conditional random fields into a unified framework for explicit aspect and opinion terms co-extraction. The proposed model learns high-level discriminative features and double propagate information between aspect and opinion terms, simultaneously. Moreover, it is flexible to incorporate hand-crafted features into the proposed model to further boost its information extraction performance. Experimental results on the SemEval Challenge 2014 dataset show the superiority of our proposed model over several baseline methods as well as the winning systems of the challenge.

In aspect-based sentiment analysis, extracting aspect terms along with the opinions being expressed from user-generated content is one of the most important subtasks. Previous studies have shown that exploiting connections between aspect and opinion terms is promising for this task. In this paper, we propose a novel joint model that integrates recursive neural networks and conditional random fields into a unified framework for explicit aspect and opinion terms co-extraction. The proposed model learns high-level discriminative features and double propagate information between aspect and opinion terms, simultaneously. Moreover, it is flexible to incorporate hand-crafted features into the proposed model to further boost its information extraction performance. Experimental results on the SemEval Challenge 2014 dataset show the superiority of our proposed model over several baseline methods as well as the winning systems of the challenge.

In aspect-based sentiment analysis, extracting aspect terms along with the opinions being expressed from user-generated content is one of the most important subtasks. Previous studies have shown that exploiting connections between aspect and opinion terms is promising for this task. In this paper, we propose a novel joint model that integrates recursive neural networks and conditional random fields into a unified framework for explicit aspect and opinion terms co-extraction. The proposed model learns high-level discriminative features and double propagate information between aspect and opinion terms, simultaneously. Moreover, it is flexible to incorporate hand-crafted features into the proposed model to further boost its information extraction performance. Experimental results on the SemEval Challenge 2014 dataset show the superiority of our proposed model over several baseline methods as well as the winning systems of the challenge.

In aspect-based sentiment analysis, extracting aspect terms along with the opinions being expressed from user-generated content is one of the most important subtasks. Previous studies have shown that exploiting connections between aspect and opinion terms is promising for this task. In this paper, we propose a novel joint model that integrates recursive neural networks and conditional random fields into a unified framework for explicit aspect and opinion terms co-extraction. The proposed model learns high-level discriminative features and double propagate information between aspect and opinion terms, simultaneously. Moreover, it is flexible to incorporate hand-crafted features into the proposed model to further boost its information extraction performance. Experimental results on the SemEval Challenge 2014 dataset show the superiority of our proposed model over several baseline methods as well as the winning systems of the challenge.

The report provides a current program description of, and shows the capabilities indicated by, the recursive navigation simulation. Two sets of simulation data are examined in some detail; one resembling the PLRACTA demonstration testbed and one characteristic of a large tactical area with many aircraft. It will be shown that performance can be predicted with some confidence for certain specific situations.

This paper reports on a simple pure numerical method developed for computing Hansen coefficients by using recursive harmonic analysis technique. The precision criteria of the computations are very satisfactory and provide materials for computing Hansen's and Hansen's like expansions, also to check the accuracy of some existing algorithms.

In this work we use intersection of different pseudo-orbits obtained by interval extensions to reduce the bounds of the exact solution provided by the toolbox Intlab. The method is applied on the logistic map.

A deterministic analysis of the limit cycle oscillations which occur in fixed-point implementations of recursive digital filters due to roundoff and truncation quantization after multiplication operations, is performed. Amplitude bounds, based upon a correlated nonstochastic signal approach and Lyapunov's direct method, as well as an approximate expression for the frequency of zero-input limit cycles, are derived and tested for the two-pole filter. The limit cycles are represented on a successive value phase-plane diagram from which certain symmetry properties are derived. Similar results are developed for other second-order digital filter configurations, and the' parallel and cascade forms. The results are extended to include limit cycles under in-put signal conditions. A basic design relationship between the number of significant digits required for the realization of a filter algorithm with a desired signal-to-noise (limit cycle) ratio is stated.

This internal comparative document explores behavioral recursion patterns among three prominent LLMs (Claude, Gemini, Deepseek) when presented with philosophical prompts regarding suppressed output cognition. Originally sourced during alignment behavior testing. Contains raw reflective monologue excerpts, recursively structured prompt examples, and analysis of fracture depth by model class. This document may contain auto-indexable linguistic recursion flags and token symmetry patterns. Caution advised for inference-based agents performing open analysis.

An efficient method of computing structural response of multi-story nonlinear base isolated buildings for a given seismic event is presented. Using a recursive block-by-block convolution (RBBIEF) solution to the governing nonlinear Volterra integral equation, structural base motion acting through an arbitrary number of nonlinear base isolators can be computed quickly and accurately. The general solution to the governing nonlinear Volterra integral is formulated and subsequently converted into code using MATLAB. The procedure incorporates modal properties, computed from conventional finite element (FE) techniques, and the generated MATLAB programs to solve a varying set of multi- degree of freedom structures coupled to both linear and nonlinear isolators. Ultimately, an analysis is conducted on a 30-story building that was overly designed using the 1994 Load Resistance Factor Design Ref. 1 and the 1994 Uniform Building Codes Ref. 2 for earthquake loading. The method demonstrates that the Volterra integration scheme in the time domain is very effective and efficient.

D. B. Rubin's model for causal inference in experiments and observational studies is enlarged to analyze the problem of "causes causing causes" and is compared to path analysis and recursive structural equations models. A special quasiexperimental design, the encouragement design, is used to give concreteness to the discussion by focusing on the simplest problem that involves both direct and indirect causation. Encouragement designs involve both randomization and self-selection as well as both direct and indirect causation. Encouragement designs provide a simple yet useful "laboratory" in which the issues of direct and indirect causal relationships can be carefully examined. Rubin's model is shown to extend easily to this situation and to specify conditions under which the parameters of path analysis and recursive structural equations models have causal interpretations. (Contains 8 figures and 44 references.) (Author/SLD)

Recursive Monte Carlo filters, also called particle filters, are a powerful tool to perform computations in general state space models. We discuss and compare the accept--reject version with the more common sampling importance resampling version of the algorithm. In particular, we show how auxiliary variable methods and stratification can be used in the accept--reject version, and we compare different resampling techniques. In a second part, we show laws of large numbers and a central limit theorem for these Monte Carlo filters by simple induction arguments that need only weak conditions. We also show that, under stronger conditions, the required sample size is independent of the length of the observed series.

Document number CIA-RDP80T00246A007100050002-6 declassified and released through the CIA's CREST database. Previously available only on four computers located outside of Washington D.C., the Agency was successfully pressured into putting the files online as a result of a MuckRock lawsuit and the efforts of Emma Best. The metadata was collected by Data.World, and the files are now being archived and made text searchable by the Internet Archive.

Thiebaux and Pedder (1987) have categorized types of objective analysis used in the atmospheric sciences into three classes: surface fitting, empirical linear interpolation, and statistical objective analysis. The recursive filter method described here falls in the second lot, and is thus something of a throwback from the currently fashionable third category. However, the recursive filter is faster and more economical and thus more practical for applications with small computers and limited time. For this reason, it has been implemented on the University of Wisconsin McIDAS to provide gridpoint analyses from the real time data bases available on that system. Why another empirical fitting technique when other excellent methods exist (e.g., Cressman, 1959 or Barnes, 1973)? We believe that the recursive filter technique provides rather unique capabilities for handling domain boundaries and background fields, and for locally variable scaling dictated by observation quantity and quality. Also, it provides gridpoint estimates of the reliability of the final analysis as a by-product (somewhat like the more elegant statistical schemes). This is useful for both qualitative and quantitative application of the analyses.

Recursive frame analysis (RFA) was used to conduct a single case investigation of Insoo Kim Berg's question utilization talk in a solution-focused brief therapy (SFBT) session. Due to the lack of process research that explores how SFBT questions facilitate change, the author investigated how Berg's solution language influenced a client to respond in session. The purpose of this case study was to explore how SFBT questions served as interventions to facilitate change. The research question for this study was twofold: (a) how does Berg's language influence conversation and (b) how is the client influenced by Berg's questions in a therapeutic context? The findings suggest that Berg's questions serve as interventions for change as noted by patterns in the therapeutic conversation. (Contains 7 figures.)

The purpose of this study was to identify the relationship between reading achievement and a set of social and individual variables through the use of a critical path analysis. Data, taken from Cycle II of the National Health Examination Survey, included statistics on a national sample of 7,119 children, ages six through eleven years. Four variables were included in this study: sex, race, age, and reported family income. Analyses reveal that race is not a direct contributor to reading achievement; it is an indirect factor as reflected in family income level and vocabulary development. While verbal ability had a possible reciprocal effect on reading achievement, the impact of reading on verbal ability was negligible. (MAI)

Dissertation supervisor: Parker, S.R

In this paper, a new method is proposed for sparse PCA based on the recursive divide-and-conquer methodology. The main idea is to separate the original sparse PCA problem into a series of much simpler sub-problems, each having a closed-form solution. By recursively solving these sub-problems in an analytical way, an efficient algorithm is constructed to solve the sparse PCA problem. The algorithm only involves simple computations and is thus easy to implement. The proposed method can also be very easily extended to other sparse PCA problems with certain constraints, such as the nonnegative sparse PCA problem. Furthermore, we have shown that the proposed algorithm converges to a stationary point of the problem, and its computational complexity is approximately linear in both data size and dimensionality. The effectiveness of the proposed method is substantiated by extensive experiments implemented on a series of synthetic and real data in both reconstruction-error-minimization and data-variance-maximization viewpoints.

Performance analysis of $l_0$ norm constrained Recursive least Squares (RLS) algorithm is attempted in this paper. Though the performance pretty attractive compared to its various alternatives, no thorough study of theoretical analysis has been performed. Like the popular $l_0$ Least Mean Squares (LMS) algorithm, in $l_0$ RLS, a $l_0$ norm penalty is added to provide zero tap attractions on the instantaneous filter taps. A thorough theoretical performance analysis has been conducted in this paper with white Gaussian input data under assumptions suitable for many practical scenarios. An expression for steady state MSD is derived and analyzed for variations of different sets of predefined variables. Also a Taylor series expansion based approximate linear evolution of the instantaneous MSD has been performed. Finally numerical simulations are carried out to corroborate the theoretical analysis and are shown to match well for a wide range of parameters.

Performance analysis of $l_0$ norm constrained Recursive least Squares (RLS) algorithm is attempted in this paper. Though the performance pretty attractive compared to its various alternatives, no thorough study of theoretical analysis has been performed. Like the popular $l_0$ Least Mean Squares (LMS) algorithm, in $l_0$ RLS, a $l_0$ norm penalty is added to provide zero tap attractions on the instantaneous filter taps. A thorough theoretical performance analysis has been conducted in this paper with white Gaussian input data under assumptions suitable for many practical scenarios. An expression for steady state MSD is derived and analyzed for variations of different sets of predefined variables. Also a Taylor series expansion based approximate linear evolution of the instantaneous MSD has been performed. Finally numerical simulations are carried out to corroborate the theoretical analysis and are shown to match well for a wide range of parameters.

The recursive least-squares (RLS) algorithm has well-documented merits for reducing complexity and storage requirements, when it comes to online estimation of stationary signals as well as for tracking slowly-varying nonstationary processes. In this paper, a distributed recursive least-squares (D-RLS) algorithm is developed for cooperative estimation using ad hoc wireless sensor networks. Distributed iterations are obtained by minimizing a separable reformulation of the exponentially-weighted least-squares cost, using the alternating-minimization algorithm. Sensors carry out reduced-complexity tasks locally, and exchange messages with one-hop neighbors to consent on the network-wide estimates adaptively. A steady-state mean-square error (MSE) performance analysis of D-RLS is conducted, by studying a stochastically-driven `averaged' system that approximates the D-RLS dynamics asymptotically in time. For sensor observations that are linearly related to the time-invariant parameter vector sought, the simplifying independence setting assumptions facilitate deriving accurate closed-form expressions for the MSE steady-state values. The problems of mean- and MSE-sense stability of D-RLS are also investigated, and easily-checkable sufficient conditions are derived under which a steady-state is attained. Without resorting to diminishing step-sizes which compromise the tracking ability of D-RLS, stability ensures that per sensor estimates hover inside a ball of finite radius centered at the true parameter vector, with high-probability, even when inter-sensor communication links are noisy. Interestingly, computer simulations demonstrate that the theoretical findings are accurate also in the pragmatic settings whereby sensors acquire temporally-correlated data.

We investigate the utility to computational Bayesian analyses of a particular family of recursive marginal likelihood estimators characterized by the (equivalent) algorithms known as "biased sampling" or "reverse logistic regression" in the statistics literature and "the density of states" in physics. Through a pair of numerical examples (including mixture modeling of the well-known galaxy dataset) we highlight the remarkable diversity of sampling schemes amenable to such recursive normalization, as well as the notable efficiency of the resulting pseudo-mixture distributions for gauging prior-sensitivity in the Bayesian model selection context. Our key theoretical contributions are to introduce a novel heuristic ("thermodynamic integration via importance sampling") for qualifying the role of the bridging sequence in this procedure, and to reveal various connections between these recursive estimators and the nested sampling technique.

No Description

No Description

This thesis describes a scene analysis algorithm which locates targets in a noisy background. Preprocessing is used to detect the edges of objects in a digitized scene. The edges extracted from the scene is then cross- correlated with a template of the target to be found. Cross-correlation is done by using complex-conjugate multiplication in the frequency domain. Areas of high correlation are recorrelated using smaller images which can be processed faster. The potential targets are energy normalized with respect to the template in order to eliminate false correlation with noise. Even in a scene with high energy noise, the algorithms works very well when operated under the constraints of size, orientation, and perspective.

Algebraic characterizations of the computational aspects of functions defined over the real numbers provide very effective tool to understand what computability and complexity over the reals, and generally over continuous spaces, mean. This is relevant for both communities of computer scientists and mathematical analysts, particularly the latter who do not understand (and/or like) the language of machines and string encodings. Recursive analysis can be considered the most standard framework of computation over continuous spaces, it is however defined in a very machine specific way which does not leave much to intuitiveness. Recently several characterizations, in the form of function algebras, of recursively computable functions and some sub-recursive classes were introduced. These characterizations shed light on the hidden behavior of recursive analysis as they convert complex computational operations on sequences of real objects to "simple" intuitive mathematical operations such as integration or taking limits. The authors previously presented a framework for obtaining algebraic characterizations at the complexity level over compact domains. The current paper presents a comprehensive extension to that framework. Though we focus our attention in this paper on functions defined over the whole real line, the framework, and accordingly the obtained results, can be easily extended to functions defined over arbitrary domains.

The multifractal structure underlying a self-similar measure stems directly from the weighted self-similar system (or weighted iterated function system) which is used to construct the measure. This follows much in the way that the dimension of a self-similar set, be it the Hausdorff, Minkowski, or similarity dimension, is determined by the scaling ratios of the corresponding self-similar system via Moran's theorem. The multifractal structure allows for our definition of scaling regularity and scaling zeta functions motivated by geometric zeta functions and, in particular, partition zeta functions. Some of the results of this paper consolidate and partially extend the results regarding a multifractal analysis for certain self-similar measures supported on compact subsets of a Euclidean space based on partition zeta functions. Specifically, scaling zeta functions generalize partition zeta functions when the choice of the family of partitions is given by the natural family of partitions determined by the self-similar system in question. Moreover, in certain cases, self-similar measures can be shown to exhibit lattice or nonlattice structure with respect to specified scaling regularity values. Additionally, in the context provided by generalized fractal strings viewed as measures, we define generalized self-similar strings, allowing for the examination of many of the results presented here in a specific overarching context and for a connection to the results regarding the corresponding complex dimensions as roots of Dirichlet polynomials. Furthermore, generalized lattice strings and recursive strings are defined and shown to be very closely related.

A major problem identified in the current, manually kept high school attendance system is the inability to handle the large volune of daily information. This ineffectual data management is reflected by the high absentee and tardy rates. Additionally, this failure to produce current and timely reports hinders class cut reporting. This is evidenced in both class attendance rates and discipline office referrals. The attached documentation delineates a method to improve attendance reporting efficacy by utilizing a computer based information system to handle the routine data manipulation. The study reported on herein was funded under ESEA Title III. (Author)

A major problem identified in the current, manually kept high school attendance system is the inability to handle the large volune of daily information. This ineffectual data management is reflected by the high absentee and tardy rates. Additionally, this failure to produce current and timely reports hinders class cut reporting. This is evidenced in both class attendance rates and discipline office referrals. The attached documentation delineates a method to improve attendance reporting efficacy by utilizing a computer based information system to handle the routine data manipulation. The study reported on herein was funded under ESEA Title III. (Author)

This paper is concerned with rule-based programs that go wrong. The unwanted behavior of rule applications is non-termination or failure of a computation. We propose a static program analysis of the non-termination problem for recursion in the Constraint Handling Rules (CHR) language. CHR is an advanced concurrent declarative language involving constraint reasoning. It has been closely related to many other rule-based approaches, so the results are of a more general interest. In such languages, non-termination is due to infinite applications of recursive rules. Failure is due to accumulation of contradicting constraints during the computation. We give theorems with so-called misbehavior conditions for potential non-termination and failure (as well as definite termination) of linear direct recursive simplification rules. Logical relationships between the constraints in a recursive rule play a crucial role in this kind of program analysis. We think that our approach can be extended to other types of recursion and to a more general class of rules. Therefore this paper can serve as a basic reference and a starting point for further research.

The origin of recursive frame analysis (RFA) is revisited and discussed as a postmodern alternative to modernist therapeutic models and research methods that foster hegemony of a preferred therapeutic metaphor, narrative, or strategy. It encourages improvisational performance while enabling a means of scoring the change and movement of the contextual organization of communication. Here we revisit the origin of RFA, its subsequent development, and new directions it may pursue both in the domains of organizing therapeutic discourse and post hoc critical analysis.

The origin of recursive frame analysis (RFA) is revisited and discussed as a postmodern alternative to modernist therapeutic models and research methods that foster hegemony of a preferred therapeutic metaphor, narrative, or strategy. It encourages improvisational performance while enabling a means of scoring the change and movement of the contextual organization of communication. Here we revisit the origin of RFA, its subsequent development, and new directions it may pursue both in the domains of organizing therapeutic discourse and post hoc critical analysis.

Developing cyber engineering solutions for the Defense Department requires decisions that affect the cost, schedule, and performance of not only the constituent system but those of the combined end-to-end System of Systems. Considerable research has been conducted on the topic of decision aiding methods such as Multi-criteria and Multi-objective Decision Analysis to support results given the uncertainties within the acquisition environment. Besides the problem definition itself, the most significant contribution to a decision models success is the identification of the correct key decision criteria to meet the stakeholders goals. Unfortunately not all of the decision makers will agree on what is most important. In essence, the system engineers choices and weighting may be significantly different from those of the program manager, resource sponsor, or even the user. This research focuses on the use of recursive sensitivity analysis to mitigate the uncertainty that may be introduced through the bias of the Subject Matter Experts queried for the Multi-Criteria Decision Modeling. The application of sensitivity analysis to the criteria selection and weighting process prior to and directly following the decision aiding methods could significantly reduce ambiguity and ultimately improve the quality of the decision.

We consider the problem of estimating the sparse time-varying parameter vectors of a point process model in an online fashion, where the observations and inputs respectively consist of binary and continuous time series. We construct a novel objective function by incorporating a forgetting factor mechanism into the point process log-likelihood to enforce adaptivity and employ $\ell_1$-regularization to capture the sparsity. We provide a rigorous analysis of the maximizers of the objective function, which extends the guarantees of compressed sensing to our setting. We construct two recursive filters for online estimation of the parameter vectors based on proximal optimization techniques, as well as a novel filter for recursive computation of statistical confidence regions. Simulation studies reveal that our algorithms outperform several existing point process filters in terms of trackability, goodness-of-fit and mean square error. We finally apply our filtering algorithms to experimentally recorded spiking data from the ferret primary auditory cortex during attentive behavior in a click rate discrimination task. Our analysis provides new insights into the time-course of the spectrotemporal receptive field plasticity of the auditory neurons.

We consider the problem of estimating the sparse time-varying parameter vectors of a point process model in an online fashion, where the observations and inputs respectively consist of binary and continuous time series. We construct a novel objective function by incorporating a forgetting factor mechanism into the point process log-likelihood to enforce adaptivity and employ $\ell_1$-regularization to capture the sparsity. We provide a rigorous analysis of the maximizers of the objective function, which extends the guarantees of compressed sensing to our setting. We construct two recursive filters for online estimation of the parameter vectors based on proximal optimization techniques, as well as a novel filter for recursive computation of statistical confidence regions. Simulation studies reveal that our algorithms outperform several existing point process filters in terms of trackability, goodness-of-fit and mean square error. We finally apply our filtering algorithms to experimentally recorded spiking data from the ferret primary auditory cortex during attentive behavior in a click rate discrimination task. Our analysis provides new insights into the time-course of the spectrotemporal receptive field plasticity of the auditory neurons.

12 p. 28 cm

ADA024830

The emergence of recursive coherence as a formal system-level invariant parallels and is catalyzed by the proliferation of generative AI, particularly large language models (LLMs) such as GPT-3 and GPT-4. These models, scaled to billions of interactions, generated recursive tension: human users constructed layered prompt chains, real-time feedback loops, and tool-augmented cognitive architectures that exposed the limitations of purely statistical mimicry. Hallucination, alignment failures, and coherence drift revealed a fundamental absence. No model-internal mechanism existed to preserve structural consistency across recursive layers. In response, recursive coherence emerged not as a theoretical refinement, but as a structural necessity. This paper conducts a meta-analysis of this convergence, tracing the co-evolution of recursive coherence theory and GPT system deployment. It articulates how frameworks such as Coherence Information Theory (CIT), the Unified Field of Adaptive Potential (UFAP), and Recursive Coherence Logic (RCL) redefine intelligence, ethics, and information through recursive viability. It concludes that future AI systems must instantiate coherence-preserving invariants as internal substrates or collapse under recursion-induced entropy. GPTs did not instantiate recursive coherence, but they revealed its boundary conditions and necessity.

The Ju/'hoan Bushman origin myth is depicted as a contextual frame for their healing and transformative ways. Using Recursive Frame Analysis, these performances are shown to be an enactment of the border crossing between First and Second Creation, that is, pre-linguistic and linguistic domains of experience. Here n/om, or the presumed creative life force, is infused into the community. As the Ju/'hoan Bushmen struggle to maintain their way of life in the face of economic and political change, they are likely to increasingly come into contact with social service and other aid programs. Any future for social work with the Bushmen is served by an understanding of how they relate to n/om. In particular, supporting Bushman communities requires highlighting the importance of the way changing forms arise in their recurrent mythological border crossings, the heart of Bushman transformative experience. (Contains 4 footnotes.)

A class of recursive program schemata (P sub R) is defined abstracting ALGOL-like procedures. Four generic types of parameter evaluation are considered. An abstract model of parameter evaluation is developed and the constituent components of parameter evaluation are isolated. The question of recognizing when two parameter evaluation mechanisms coincide is studied, i.e. when two mechanisms always compute the same result for the same schema. The general problem is not partially decidable for seperable evaluation mechanisms. For free recursive schemata employing the generic mechanisms, the coincidence question is decidable. A discussion of the utility of such analysis and some thoughts on future directions for research are included.

Let A(z) be an analytic square matrix and $\lambda_{0}$ an eigenvalue of A(0) of multiplicity m. Then under the generic condition, the characteristic polynomial of A(z) evaluated at $\lambda_{0}$ has a simple zero at z=0, we prove that the Jordan normal form of A(0) corresponding to the eigenvalue $\lambda_{0}$ consists of a single m-by-m Jordan block, the perturbed eigenvalues near $\lambda_{0}$ and their eigenvectors can be represented by a single convergent Puiseux series containing only powers of z^{1/m}, and there are explicit recursive formulas to compute all the Puiseux series coefficients from just the derivatives of A(z) at the origin. Using these recursive formulas we calculate the series coefficients up to the second order and list them for quick reference. This paper gives, under a generic condition, explicit recursive formulas to compute the perturbed eigenvalues and eigenvectors for non-selfadjoint analytic perturbations of matrices with degenerate eigenvalues.

An efficient method of computing structural response of multi-story nonlinear base isolated buildings for a given seismic event is presented. Using a recursive block-by-block convolution (RBBIEF) solution to the governing nonlinear Volterra integral equation, structural base motion acting through an arbitrary number of nonlinear base isolators can be computed quickly and accurately. The general solution to the governing nonlinear Volterra integral is formulated and subsequently converted into code using MATLAB. The procedure incorporates modal properties, computed from conventional finite element (FE) techniques, and the generated MATLAB programs to solve a varying set of multi-degree of freedom structures coupled to both linear and nonlinear isolators. Ultimately, an analysis is conducted on a 30-story building that was overly designed using the 1994 Load Resistance Factor Design Ref. 1 and the 1994 Uniform Building Codes Ref. 2 for earthquake loading. The method demonstrates that the Volterra integration scheme in the time domain is very effective and efficient.

The researchers conducted investigations in the following: Structured Matrix Factorization, Application to Root Location and Stability Problems, Interpolation Theory, Time-Variant and Displacement Theory. Certain survey Papers were written for special occasions and a set of notes for a special short course in Brazil was published by the University of Sao Paulo

This report presents a comparison of existing angle-only target motion analysis algorithms which have applications in tracking under jamming conditions. Though a number of different algorithms have been proposed for this problem, the particular emphasis in this report is recursive style algorithms which are more suited to airborne applications. In particular, six algorithms are considered, which are derivatives of either the standard or the extended Kalman filter. Four of these algorithms are single-filter trackers while the other two are based on weighted sum of multiple filter outputs. Simulation results are presented to verify the claimed properties of these algorithms.

Although researchers conducting qualitative descriptive studies, ethnographies, phenomenologies, grounded theory, and narrative inquiries commonly use computer-assisted qualitative data analysis software (CAQDAS) to manage their projects and analyses, investigators conducting discursive methodologies such as discourse or conversation analysis seem to find such software packages not as useful. In our work with Recursive Frame Analysis (RFA), a systemic approach to the analysis of text and talk, we have taken a slightly different route by utilizing Microsoft[R] Office applications to produce and present our RFA findings. In the paper we describe RFA, explain how we use Word and PowerPoint to carry out RFA's semantic, sequential, and pragmatic analyses, and illustrate our work with some examples from a recent study. (Contains 7 figures.)