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Remote sensing visual question answering (RSVQA) has been involved in several research in recent years, leading to an increase in new methods. RSVQA automatically extracts information from satellite images, so far only optical, and a question to automatically search for the answer in the image and provide it in a textual form. In our research, we study whether Synthetic Aperture Radar (SAR) images can be beneficial to this field. We divide our study into three phases which include classification methods and VQA. In the first one, we explore the classification results of SAR alone and investigate the best method to extract information from SAR data. Then, we study the combination of SAR and optical data. In the last phase, we investigate how SAR images and a combination of different modalities behave in RSVQA compared to a method only using optical images. We conclude that adding the SAR modality leads to improved performances, although further research on using SAR data to automatically answer questions is needed as well as more balanced datasets., Comment: 6 pages, 4 figures

Visual Question Answering for Remote Sensing (RSVQA) is a task that aims at answering natural language questions about the content of a remote sensing image. The visual features extraction is therefore an essential step in a VQA pipeline. By incorporating attention mechanisms into this process, models gain the ability to focus selectively on salient regions of the image, prioritizing the most relevant visual information for a given question. In this work, we propose to embed an attention mechanism guided by segmentation into a RSVQA pipeline. We argue that segmentation plays a crucial role in guiding attention by providing a contextual understanding of the visual information, underlying specific objects or areas of interest. To evaluate this methodology, we provide a new VQA dataset that exploits very high-resolution RGB orthophotos annotated with 16 segmentation classes and question/answer pairs. Our study shows promising results of our new methodology, gaining almost 10% of overall accuracy compared to a classical method on the proposed dataset., Comment: Accepted to IGARSS 2024

This paper gives a broad account of the various sequent-based proof formalisms in the proof-theoretic literature. We consider formalisms for various modal and tense logics, intuitionistic logic, conditional logics, and bunched logics. After providing an overview of the logics and proof formalisms under consideration, we show how these sequent-based formalisms can be placed in a hierarchy in terms of the underlying data structure of the sequents. We then discuss how this hierarchy can be traversed using translations. Translating proofs up this hierarchy is found to be relatively easy while translating proofs down the hierarchy is substantially more difficult. Finally, we inspect the prevalent distinction in structural proof theory between 'internal calculi' and 'external calculi'. It is observed that these classes resist a rigorous separation, and we critically assess the properties that (calculi from) these classes are purported to possess.

We wish to define the limits of a classical classification model based on deep learning when applied to abstract images, which do not represent visually identifiable objects.QR codes (Quick Response codes) fall into this category of abstract images: one bit corresponding to one encoded character, QR codes were not designed to be decoded manually. To understand the limitations of a deep learning-based model for abstract image classification, we train an image classification model on QR codes generated from information obtained when reading a health pass. We compare a classification model with a classical (deterministic) decoding method in the presence of noise. This study allows us to conclude that a model based on deep learning can be relevant for the understanding of abstract images., Comment: in French language. RFIAP 2022 - Reconnaissance des Formes, Image, Apprentissage et Perception, Jul 2022, Vannes (Bretagne), France

We study finite first-order satisfiability (FSAT) in the constructive setting of dependent type theory. Employing synthetic accounts of enumerability and decidability, we give a full classification of FSAT depending on the first-order signature of non-logical symbols. On the one hand, our development focuses on Trakhtenbrot's theorem, stating that FSAT is undecidable as soon as the signature contains an at least binary relation symbol. Our proof proceeds by a many-one reduction chain starting from the Post correspondence problem. On the other hand, we establish the decidability of FSAT for monadic first-order logic, i.e. where the signature only contains at most unary function and relation symbols, as well as the enumerability of FSAT for arbitrary enumerable signatures. To showcase an application of Trakhtenbrot's theorem, we continue our reduction chain with a many-one reduction from FSAT to separation logic. All our results are mechanised in the framework of a growing Coq library of synthetic undecidability proofs., Comment: arXiv admin note: substantial text overlap with arXiv:2004.07390

Epilepsy is a dynamic and complex neurological disease affecting about 1% of the worldwide population, among which 30% of the patients are drug-resistant. Epilepsy is characterized by recurrent episodes of paroxysmal neural discharges (the so-called seizures), which manifest themselves through a large-amplitude rhythmic activity observed in depth-EEG recordings, in particular in local field potentials (LFPs). The signature characterizing the transition to seizures involves complex oscillatory patterns, which could serve as a marker to prevent seizure initiation by triggering appropriate therapeutic neurostimulation methods. To investigate such protocols, neurophysiological lumped-parameter models at the mesoscopic scale, namely neural mass models, are powerful tools that not only mimic the LFP signals but also give insights on the neural mechanisms related to different stages of seizures. Here, we analyze the multiple time-scale dynamics of a neural mass model and explain the underlying structure of the complex oscillations observed before seizure initiation. We investigate population-specific effects of the stimulation and the dependence of stimulation parameters on synaptic timescales. In particular, we show that intermediate stimulation frequencies (>20 Hz) can abort seizures if the timescale difference is pronounced. Those results have the potential in the design of therapeutic brain stimulation protocols based on the neurophysiological properties of tissue.

We study finite first-order satisfiability (FSAT) in the constructive setting of dependent type theory. Employing synthetic accounts of enumerability and decidability, we give a full classification of FSAT depending on the first-order signature of non-logical symbols. On the one hand, our development focuses on Trakhtenbrot's theorem, stating that FSAT is undecidable as soon as the signature contains an at least binary relation symbol. Our proof proceeds by a many-one reduction chain starting from the Post correspondence problem. On the other hand, we establish the decidability of FSAT for monadic first-order logic, i.e. where the signature only contains at most unary function and relation symbols, as well as the enumerability of FSAT for arbitrary enumerable signatures. All our results are mechanised in the framework of a growing Coq library of synthetic undecidability proofs.

We formalise the undecidability of solvability of Diophantine equations, i.e. polynomial equations over natural numbers, in Coq's constructive type theory. To do so, we give the first full mechanisation of the Davis-Putnam-Robinson-Matiyasevich theorem, stating that every recursively enumerable problem -- in our case by a Minsky machine -- is Diophantine. We obtain an elegant and comprehensible proof by using a synthetic approach to computability and by introducing Conway's FRACTRAN language as intermediate layer. Additionally, we prove the reverse direction and show that every Diophantine relation is recognisable by $\mu$-recursive functions and give a certified compiler from $\mu$-recursive functions to Minsky machines.

The shape that stem cells reach at the end of adhesion process influences their differentiation. Rearrangement of cytoskeleton and modification of intracellular tension may activate mechanotransduction pathways controlling cell commitment. In the present study, the mechanical signals involved in cell adhesion were computed in in vitro stem cells of different shapes using a single cell model, the so-called Cytoskeleton Divided Medium (CDM) model. In the CDM model, the filamentous cytoskeleton and nucleoskeleton networks were represented as a mechanical system of multiple tensile and compressive interactions between the nodes of a divided medium. The results showed that intracellular tonus, focal adhesion forces as well as nuclear deformation increased with cell spreading. The cell model was also implemented to simulate the adhesion process of a cell that spreads on protein-coated substrate by emitting filopodia and creating new distant focal adhesion points. As a result, the cell model predicted cytoskeleton reorganisation and reinforcement during cell spreading. The present model quantitatively computed the evolution of certain elements of mechanotransduction and may be a powerful tool for understanding cell mechanobiology and designing biomaterials with specific surface properties to control cell adhesion and differentiation.

Graph theoretical approach has proved an effective tool to understand, characterize and quantify the complex brain network. However, much less attention has been paid to methods that quantitatively compare two graphs, a crucial issue in the context of brain networks. Comparing brain networks is indeed mandatory in several network neuroscience applications. Here, we discuss the current state of the art, challenges, and a collection of analysis tools that have been developed in recent years to compare brain networks. We first introduce the graph similarity problem in brain network application. We then describe the methodological background of the available metrics and algorithms of comparing graphs, their strengths and limitations. We also report results obtained in concrete applications from normal brain networks. More precisely, we show the potential use of brain network similarity to build a 'network of networks' that may give new insights into the object categorization in the human brain. Additionally, we discuss future directions in terms of network similarity methods and applications., Comment: 31 pages, 8 figures, 4 tables

Identifying the physiological processes underlying the emergence and maintenance of consciousness is one of the most fundamental problems of neuroscience, with implications ranging from fundamental neuroscience to the treatment of patients with disorders of consciousness (DOC). One major challenge is to understand how cortical circuits at drastically different spatial scales, from local networks to brain-scale networks, operate in concert to enable consciousness, and how those processes are impaired in DOC patients. In this review, we attempt to relate available neurophysiological and clinical data with existing theoretical models of consciousness, while linking the micro- and macro-circuit levels. First, we address the relationships between awareness and wakefulness on the one hand, and cortico-cortical, and thalamo-cortical connectivity on the other hand. Second, we discuss the role of three main types of GABAergic interneurons in specific circuits responsible for the dynamical re-organization of functional networks. Third, we explore advances in the functional role of nested oscillations for neural synchronization and communication, emphasizing the importance of the balance between local (high-frequency) and distant (low-frequency) activity for efficient information processing. The clinical implications of these theoretical considerations are presented. We propose that such cellular-scale mechanisms could extend current theories of consciousness.

The quality of plastic parts produced through injection molding depends on many factors. Especially during the filling stage, defects such as weld lines, burrs, or insufficient filling can occur. Numerical methods need to be employed to improve product quality by means of predicting and simulating the injection molding process. In the current work, a highly viscous incompressible non-isothermal two-phase flow is simulated, which takes place during the cavity filling. The injected melt exhibits a shear-thinning behavior, which is described by the Carreau-WLF model. Besides that, a novel discretization method is used in the context of 4D simplex space-time grids [2]. This method allows for local temporal refinement in the vicinity of, e.g., the evolving front of the melt [10]. Utilizing such an adaptive refinement can lead to locally improved numerical accuracy while maintaining the highest possible computational efficiency in the remaining of the domain. For demonstration purposes, a set of 2D and 3D benchmark cases, that involve the filling of various cavities with a distributor, are presented., Comment: 14 pages, 11 Figures, 4 Tables

Emerging evidence shows that the modular organization of the human brain allows for better and efficient cognitive performance. Many of these cognitive functions are very fast and occur in subsecond time scale such as the visual object recognition. Here, we investigate brain network modularity while controlling stimuli meaningfulness and measuring participant reaction time. We particularly raised two questions: i) does the dynamic brain network modularity change during the recognition of meaningful and meaningless visual images? And ii) is there a correlation between network modularity and the reaction time of participants? To tackle these issues, we collected dense electroencephalography (EEG, 256 channels) data from 20 healthy human subjects performing a cognitive task consisting of naming meaningful (tools, animals) and meaningless (scrambled) images. Functional brain networks in both categories were estimated at subsecond time scale using the EEG source connectivity method. By using multislice modularity algorithms, we tracked the reconfiguration of functional networks during the recognition of both meaningful and meaningless images. Results showed a difference in the module characteristics of both conditions in term of integration (interactions between modules) and occurrence (probability on average of any two brain regions to fall in the same module during the task). Integration and occurrence were greater for meaningless than for meaningful images. Our findings revealed also that the occurrence within the right frontal regions and the left occipito-temporal can help to predict the ability of the brain to rapidly recognize and name visual stimuli. We speculate that these observations are applicable not only to other fast cognitive functions but also to detect fast disconnections that can occur in some brain disorders.

Emerging evidence shows that cognitive deficits in Alzheimer disease (AD) are associated with disruptions in brain functional connectivity. Thus, the identification of alterations in AD functional networks has become a topic of increasing interest. However, to what extent AD induces disruption of the balance of local and global information processing in the human brain remains elusive. The main objective of this study is to explore the dynamic topological changes of AD networks in terms of brain network segregation and integration. We used electroencephalography (EEG) data recorded from 20 participants (10 AD patients and 10 healthy controls) during resting state. Functional brain networks were reconstructed using EEG source connectivity computed in different frequency bands. Graph theoretical analyses were performed assess differences between both groups. Results revealed that AD networks, compared to networks of age matched healthy controls, are characterized by lower global information processing (integration) and higher local information processing (segregation). Results showed also significant correlation between the alterations in the AD patients functional brain networks and their cognitive scores. These findings may contribute to the development of EEG network-based test that could strengthen results obtained from currently used neurophysiological tests in neurodegenerative diseases.

The human brain is a large-scale network which function depends on dynamic interactions between spatially-distributed regions. In the rapidly-evolving field of network neuroscience, two yet unresolved challenges are potential breakthroughs. First, functional brain networks should be estimated from noninvasive and easy to use neuroimaging techniques. Second, the time/space resolution of these techniques should be high enough to assess the dynamics of identified networks. Emerging evidence suggests that Electroencephalography (EEG) source connectivity method may offer solutions to both issues provided that scalp EEG signals are appropriately processed. Therefore, the performance of EEG source connectivity method strongly depends on signal processing (SP) that involves various methods such as preprocessing techniques, inverse solutions, statistical couplings between signals and network science. The main objective of this tutorial-like review is to provide an overview on EEG source connectivity. We describe the major contributions that the SP community brought to this research field. We emphasize the methodological issues that need to be carefully addressed to obtain relevant results and we stress the current limitations that need further investigation. We also report results obtained in concrete applications, in both normal and pathological brain states. Future directions in term of signal processing methods and applications are eventually provided, Comment: Accepted in the IEEE Signal Processing Magazine

Quantifying the similarity between two networks is critical in many applications. A number of algorithms have been proposed to compute graph similarity, mainly based on the properties of nodes and edges. Interestingly, most of these algorithms ignore the physical location of the nodes, which is a key factor in the context of brain networks involving spatially defined functional areas. In this paper, we present a novel algorithm called "SimiNet" for measuring similarity between two graphs whose nodes are defined a priori within a 3D coordinate system. SimiNet provides a quantified index (ranging from 0 to 1) that accounts for node, edge and spatiality features. Complex graphs were simulated to evaluate the performance of SimiNet that is compared with eight state-of-art methods. Results show that SimiNet is able to detect weak spatial variations in compared graphs in addition to computing similarity using both nodes and edges. SimiNet was also applied to real brain networks obtained during a visual recognition task. The algorithm shows high performance to detect spatial variation of brain networks obtained during a naming task of two categories of visual stimuli: animals and tools. A perspective to this work is a better understanding of object categorization in the human brain.

Cognition involves dynamic reconfiguration of functional brain networks at sub-second time scale. A precise tracking of these reconfigurations to categorize visual objects remains elusive. Here, we use dense electroencephalography (EEG) data recorded during naming meaningful (tools, animals) and scrambled objects from 20 healthy subjects. We combine technique for identifying functional brain networks and recently developed algorithm for estimating networks similarity to discriminate between the two categories. First, we showed that dynamic networks of both categories can be segmented into several brain network states (times windows with consistent brain networks) reflecting sequential information processing from object representation to reaction time. Second, using a network similarity algorithm, results showed high intra-category and very low inter-category values. An average accuracy of 76% was obtained at different brain network states., Comment: 4 pages, 2 figures, ICABME 2017 conference

Epilepsy is a network disease. The epileptic network usually involves spatially distributed brain regions. In this context, noninvasive M/EEG source connectivity is an emerging technique to identify functional brain networks at cortical level from noninvasive recordings. In this paper, we analyze the effect of the two key factors involved in EEG source connectivity processing: i) the algorithm used in the solution of the EEG inverse problem and ii) the method used in the estimation of the functional connectivity. We evaluate four inverse solutions algorithms and four connectivity measures on data simulated from a combined biophysical/physiological model to generate realistic interictal epileptic spikes reflected in scalp EEG. We use a new network-based similarity index (SI) to compare between the network identified by each of the inverse/connectivity combination and the original network generated in the model. The method will be also applied on real data recorded from one epileptic patient who underwent a full presurgical evaluation for drug-resistant focal epilepsy. In simulated data, results revealed that the selection of the inverse/connectivity combination has a significant impact on the identified networks. Results suggested that nonlinear methods for measuring the connectivity are more efficient than the linear one. The wMNE inverse solution showed higher performance than dSPM, cMEM and sLORETA. In real data, the combination (wMNE/PLV) led to a very good matching between the interictal epileptic network identified from noninvasive EEG recordings and the network obtained from connectivity analysis of intracerebral EEG recordings. These results suggest that source connectivity method, when appropriately configured, is able to extract highly relevant diagnostic information about networks involved in interictal epileptic spikes from non-invasive dense-EEG data., Comment: 30 pages, 5 figures

Exploring the human brain networks during rest is a topic of great interest. Several structural and functional studies have previously been conducted to study the intrinsic brain networks. In this paper, we focus on investigating the human brain network topology using dense Electroencephalography (EEG) source connectivity approach. We applied graph theoretical methods on functional networks reconstructed from resting state data acquired using EEG in 14 healthy subjects. Our findings confirmed the existence of sets of brain regions considered as functional hubs. In particular, the isthmus cingulate and the orbitofrontal regions reveal high levels of integration. Results also emphasize on the critical role of the default mode network (DMN) in enabling an efficient communication between brain regions., Comment: International Conference on Bio-engineering for Smart Technologies (BioSMART 2016)

Network modularity is a key feature for efficient information processing in the human brain. This information processing is however dynamic and networks can reconfigure at very short time period, few hundreds of millisecond. This requires neuroimaging techniques with sufficient time resolution. Here we use the dense electroencephalography, EEG, source connectivity methods to identify cortical networks with excellent time resolution, in the order of millisecond. We identify functional networks during picture naming task. Two categories of visual stimuli were presented, meaningful (tools, animals) and meaningless (scrambled) objects. In this paper, we report the reconfiguration of brain network modularity for meaningful and meaningless objects. Results showed mainly that networks of meaningful objects were more modular than those of meaningless objects. Networks of the ventral visual pathway were activated in both cases. However a strong occipitotemporal functional connectivity appeared for meaningful object but not for meaningless object. We believe that this approach will give new insights into the dynamic behavior of the brain networks during fast information processing., Comment: The 3rd Middle East Conference on Biomedical Engineering (MECBME'16)

A major challenge in cognitive neuroscience is to evaluate the ability of the human brain to categorize or group visual stimuli based on common features. This categorization process is very fast and occurs in few hundreds of millisecond time scale. However, an accurate tracking of the spatiotemporal dynamics of large-scale brain networks is still an unsolved issue. Here, we show the combination of recently developed method called dense-EEG source connectivity to identify functional brain networks with excellent temporal and spatial resolutions and an algorithm, called SimNet, to compute brain networks similarity. Two categories of visual stimuli were analysed in this study: immobile and mobile. Networks similarity was assessed within each category (intra-condition) and between categories (inter-condition). Results showed high similarity within each category and low similarity between the two categories. A significant difference between similarities computed in the intra and inter-conditions was observed at the period of 120-190ms supposed to be related to visual recognition and memory access. We speculate that these observations will be very helpful toward understanding the object categorization in the human brain from a network perspective., Comment: 5 pages, 2 figures. Accepted for 2016 IEEE Workshop on Statistical Signal Processing

In order to understand the mechanisms involved in the forming step of LCM processes and provide validation date to numerical models, a specific experimental device has been designed in collaboration between PRISME Institute and EADS. The goal of this paper is to present the potentialities and the first results obtained with this device., Comment: 17th International Conference on Composite Materials, Edinburgh : United Kingdom (2010)

In order to understand the mechanisms involved in the forming step of LCM processes and provide validation data to numerical models, a specific experimental device has been designed in collaboration between PRISME Institute and EADS. This toot also makes it possible to test the feasibility to obtain specific double curved shape constituted with dry fabric reinforcement. It contains one mechanical module containing the classical tools in forming process, (punch, blank holder, and open-die), and one optical module to measure the 3D-deformed shape and the distribution of local deformations, like shear angles of the woven reinforcement during all the process. The goal of this paper is to present the potentialities and the first results obtained with this device., Comment: 14th European Conference on Composite Materials, Budapest : Hungary (2010)

The first true coaxial 18-fold segmented n-type HPGe prototype detector produced by Canberra-France for the GERDA neutrinoless double beta-decay project was tested both at Canberra-France and at the Max-Planck-Institut fuer Physik in Munich. The main characteristics of the detector are given and measurements concerning detector properties are described. A novel method to establish contacts between the crystal and a Kapton cable is presented., Comment: 21 pages, 16 Figures, to be submitted to NIM A

The Internet is becoming the primary way that businesses communicate and receive information. Corporate Web addresses and home pages have become a valuable tool for leaving a solid mark on potential clients, consumers, and competition. To determine how differences in Web pages design reflect corporate image, a study examined Web pages from two organizational angles: professional public relations (PR) firms and retail corporations. Content analysis used a sample size consisting of eight corporations and eight PR firms, observing the various Web pages for a month. Results indicated corporations used much more elaborate and advanced graphics than did PR firms; corporations used moving graphics, highly technical pictures, and hand drawings, while PR companies relied more heavily on text to attract potential customers. The two groups also used different strategies in web page color. Corporations used many vibrant colors, while PR firms chose a simpler, more conservative color scheme. Both groups, however, used color to accomplish a certain theme. In the case of Web page link analysis, corporations used more links than PR firms did: corporations had 26 links per address while PR firms had 17 links. Corporations used external links more than PR firms did: only half the PR firms included links beyond their own organization, while every corporation used external links. Corporation links were also found to be more user-friendly. Future research may include how intended publics receive information from Web sites. By studying how effective Web pages are to the consumers, organizations can learn how to market themselves effectively to their target audience. (Contains 14 references and list of Web pages analyzed). (NKA)

This paper describes the process that school-based and university-based educators followed to transform a long-term cooperative relationship into a professional development school community. The school-university partnership involved teachers and staffs from four elementary schools, and faculty and graduate students from the language, literature, and reading program at Ohio State University. (SM)

The economic benefits of NASA programs are discussed. Emphasis is given to an analysis of indirect economic benefits which estimates the effect of NASA programs on employment, personal income, corporate sales and profits, and government tax revenues in the U.S. and in each state. Data are presented that show that NASA programs have widely varying multipliers by industry and that illustrate the distribution of jobs by industry as well as the distribution of sales.

The private-sector economic and employment impacts of the U.S. Space Program on the whole nation and on each state were investigated, considering 80 industries and 475 occupations. It is shown that the FY 1987 NASA procurement expenditures of $8.6 billion resulted in 209,000 jobs, $17.8 billion in total industry sales, $2.9 billion in corporate profits, and $5.6 billion in Federal, state, and local government tax revenues. Although the industry and job impacts are concentrated in states that are the largest direct recipients of NASA program funds (California, Texas, and Florida), every state in the nation benefits from the Space Program and the indirect economic benefits for many states are very high. Details are presented of the total sales and jobs created in each state.

This report is divided into four sections. Section 1 is a short discussion of the economic theory underlying the construction of the cost of education index and an example of how the index is calculated. Also presented are descriptions of the factors included in the statistical analysis to control for quality, quantity, and cost differences and descriptions of the data used to evaluate these relations. Results of the calculation of the cost of education index for a sample of New York state school districts are presented in Section 2. The agreement among alternative methods of calculating the index and its stability across years are also evaluated. Section 3 addresses a number of questions about the cost index methodology, such as multiple measures for the same phenomenon and the number of variables in the specification. Section 4 analyzes some of the policy considerations and implications for New York State. Appendixes contain tables of determinants of costs, an index for the state, and the computation of a salary index. (Author/IRT)

Variations in public services and tax rates among communities may cause property values to vary, a concept referred to by economists as capitalization. The purpose of this booklet is to describe and evaluate the implications of capitalization primarily for school finance changes and secondarily for property tax changes. Section 1 describes the conditions necessary for capitalization and the process of capitalization itself. Section 2 presents several theories discussed in the literature and several studies that have measured the magnitude of capitalization. Section 3 analyzes both the short- and long-term capitalization effects of specific changes in state aid with attention to aid for public schools. The appendix offers an annotated bibliography. (Author/LD)

Current methods of funding teachers' retirement systems, which base pensions on final salaries, are inequitable because they are not related to school districts' ability to pay and because they require some teachers to subsidize others. A five-state survey shows it is common for pensions to be funded by school districts and teachers, sometimes with help from the state. Data from one of the states, New York, show, first, that districts have no control over many factors affecting their salary levels, including their labor market area, percentage of special students, and size. Second, they indicate that variation among districts in their rates of salary change over time is a more important source of inequity than differences in salary schedules. Two conclusions can be drawn: (1) that pension funding based on districts' salary costs is unfair for districts with low property wealth, because districts cannot control these costs; and (2) that teachers from districts with low rates of salary change are subsidizing teachers from districts with high rates of change. One recommendation for reducing these inequities is to tie retirees' pension payments to years of experience and age and not to final salary levels. (RW)

Increased educational resources at home and at school will improve student achievement, according to this study of third graders' reading and math scores in 384 New York State public schools. The factors tested included the school environment (teachers' years of experience and amount of graduate training, school size, the student-teacher ratio, the percentage of minority students, and expenditures per student) and the home environment (indicated by the school-district population's median years of schooling and percentage living below the poverty line). Schools studied were in New York City, upstate cities, and suburban and rural areas. Achievement test scores showed positive statistical associations with the schools' level of teacher experience and expenditure per student, and a negative association with schools' percentage of minority students. Achievement scores showed stronger associations with home influences--a positive one with the population's years of schooling and a negative one with percentage below poverty. Rural schools had higher achievement scores than expected. The study concludes that additional financing for schools with low expenditures per student will raise achievement levels. Further research is needed to identify home conditions most easily influenced by schools and the factors responsible for rural schools' superior performance. (RW)

A review of the major studies of administrative behavior introduces this consideration of what features of administrative agencies, either in structure or patterns of organization, have been associated with their successful operation. The theories of interorganizational relations reviewed include the political economy approach, the organization set model, the exchange theory, the power dependency approach, and an integrated model employing both the exchange and power dependency theories. Two theories, the political economy approach and the organization set model, are helpful in examining case studies of the behavior of several administrative agencies, including the Federal Trade Commission, the Food and Drug Administration, and two Public Relations Employment Boards (PERBs), and in predicting the response of the network of organizations to certain changes. PERBs are examined in light of five hypotheses about interorganizational relationships proposed by William Evan. The paper concludes that the organization set approach seems to be the most appropriate construct for analyzing relationships among school district employees, school districts, and the administrative agency. Suggestions are offered for establishing the autonomy of PERBs while maintaining accountability, for controlling the size of PERBs, and for appointing commissioners and other personnel. (Author/WD)

This study is based on a concern that secondary education students, when preparing to teach in the broad field of social studies, typically major in a narrowly defined discipline such as psychology, economics, history, or political science. The purpose of this study is twofold: (1) to determine how well students are academically prepared to teach in the broad area of social studies, and (2) to examine students' courses of study to determine patterns of preparation that better prepare them for the student teaching experience. If one assumes that the social studies specialty area examination reflects a broad field of studies, then successful achievement on the test would mean that students are adequately prepared to teach these subjects. However, it is possible that high scores on the National Teacher's Examination (NTE), recorded for the secondary social studies student teachers enrolled in the University of Washington Teacher Education Program, result from completion by these students of nearly twice the required number of courses for the major. Given the results of this study, the rigorous admission requirements for the University of Washington's Teacher Education Program, the preponderance of fifth-year students, and the social studies core requirement, it is reasonable to assume that the narrowly defined academic majors do not present a serious problem for University of Washington students who are preparing to teach in the broader field of social studies. (BZ)