Miracle-dz/UniFAR-FTUs huggingface dataset:
Interactive minds : life-span perspectives on the social foundation of cognition

Part I. General Theoretical Framework: 1. The evolution of cooperation: within and between generations Peter Hammerstein 2. Interacting minds in a lifespan perspective: a cultural-historical approach to culture and cognitive development Michael Cole 3. Essentially Social: on the origin of linguistic knowledge in the individual Wolfgang Klein 4. Knowledge and the construction of women's development Gisela Labouvie-Vief Part II. Interactive Minds: 5. Peer interactive minds: development, theoretical, and methodological issues Margarita Azmitia 6. Collaborative rules: how are people supposed to work with one another Jacqueline Goodnow 7. The lifelong transformation of moral goals through social influence Damon William 8. Adults Telling and retelling stories collaboratively Roger A. Dixon 9. Planning about life: towards a social-interactive paradigm Jacqui Smith 10. Wisdom and the social-interactive foundation of the mind Ursula Staudinger Part III. Interactive Minds: Perspecxtives from Cognitive and Educational Psychology: 11. Rationality: Why Social Context Matters Gerd Gigerenzer 12. Styles of thinking Robert J. Sternberg 13. Cooperative construction of expert knowledge: the case of knowledge engineering Gerhard Strube 14. Communities of practice towards expertise: social foundation of university instruction Heinz Mandl Epilogue: reflections and future perspectives 15. Cognitive pychology Peter Graf 16. Developmental psychology Laura Carstensen 17. Educational psychology Franz Weinert 18. Cognitive and cultural anthropology Richard Shweder.
Incremental upgrade checking by means of interpolation-based function summaries

During its evolution, a typical software/hardware design undergoes a myriad of small changes. However, it is extremely costly to verify each new version from scratch. As a remedy to this problem, we propose to use function summaries to enable incremental verification of the evolving systems. During the evolution, our approach maintains function summaries derived using Craig's interpolation. For each new version, these summaries are used to perform a local incremental check. Benefit of this approach is that the cost of the check depends on the extent of the change between the two versions and can be performed cheaply for incremental changes without resorting to re-verification of the entire system. Our implementation and experimentation in the context of the bounded model checking for C confirms that incremental changes can be verified efficiently for different classes of industrial programs.
Mining time-dependent influential users in Facebook fans group

Klout, a famous App, could measure people's social network influence power. Klout score is measured according to the data from past 90 days and an individual who has high Klout score is thought as having high social influence power. Lots of businesses or organizations like to hire high Klout score people to help them to diffuse their brand images. However, Klout score cannot tell us who has high influence power in a specific short time period. For example, it is possible that some of the users might always have high influence power on Monday or on Monday morning. These time-dependent influential users probably have low Klout scores in average but have high influence power in some specific time periods. Businesses should not just know who are the high Klout score users but also they should identify who are the time-dependent influential users because all of them may have some sort of power to influence other users' buying decisions. In this study, a framework based on frequent pattern mining is proposed to find the time-dependent influential users. First of all, the framework will divide a predefined long time period into successive short time segments and then influential transactions that contain Facebook fans' influence power data will be defined in each time segment. From the frequent patterns, the proper time for time-dependent influence users to spread information can be found. A theoretical experiment is given to verify the effectiveness of the proposed framework.
Outcomes Prediction via Time Intervals Related Patterns

The increasing availability of multivariate temporal data in many domains, such as biomedical, security and more, provides exceptional opportunities for temporal knowledge discovery, classification and prediction, but also challenges. Temporal variables are often sparse and in many domains, such as in biomedical data, they have huge number of variables. In recent decades in the biomedical domain events, such as conditions, drugs and procedures, are stored as time intervals, which enables to discover Time Intervals Related Patterns (TIRPs) and use for classification or prediction. In this study we present a framework for outcome events prediction, called Maitreya, which includes an algorithm for TIRPs discovery called KarmaLegoD, designed to handle huge number of symbols. Three indexing strategies for pairs of symbolic time intervals are proposed and compared, showing that the use of FullyHashed indexing is only slightly slower but consumes minimal memory. We evaluated Maitreya on eight real datasets for the prediction of clinical procedures as outcome events. The use of TIRPs outperform the use of symbols, especially with horizontal support (number of instances) as TIRPs feature representation.
Women in Western Political Thought

The silencing of women writers and denigration of the feminine in the canon of western political thought has marked feminism's relationship to canonical texts as deeply ambivalent. And yet, conversations and debates initiated amongst feminists in response have been lively and provocative. Though it often seems that women are simply absent from political theory's canonical texts, in fact these texts frequently operate on gendered assumptions. The kind of person qualified for citizenship, the traits and behaviors a citizen should embody, what constitutes the geography and categorization of the political, how political life should be structured and conducted, and even what makes for a good and worthy life are all described in terms that characterize masculine traits as superior. Keywords: feminism; political theory; women's rights
A system dynamics approach to land use transport interaction modelling: the strategic model MARS and its application

Land use transport interaction models have been developed in various forms dating back to the early 1960s. They have moved from spatial interaction models or statistical models through econometric models to micro-simulation, cell-based automata and agent-based models. There has been a move towards more detail in representing space and individual behaviour. This paper presents an alternative approach which uses an intentionally developed strategic high-level model. Nevertheless this model still delivers comparable levels of statistical fi t in terms of validation but is easy to present to decision makers and planners. The paper introduces the concepts behind the MARS model, deals with validation and transferability between cities and provides example applications. Copyright © 2010 John Wiley & Sons, Ltd.
Vital dye analysis of cranial neural crest cell migration in the mouse embryo.

The spatial and temporal aspects of cranial neural crest cell migration in the mouse are poorly understood because of technical limitations. No reliable cell markers are available and vital staining of embryos in culture has had limited success because they develop normally for only 24 hours. Here, we circumvent these problems by combining vital dye labelling with exo utero embryological techniques. To define better the nature of cranial neural crest cell migration in the mouse embryo, premigratory cranial neural crest cells were labelled by injecting DiI into the amniotic cavity on embryonic day 8. Embryos, allowed to develop an additional 1 to 5 days exo utero in the mother before analysis, showed distinct and characteristic patterns of cranial neural crest cell migration at the different axial levels. Neural crest cells arising at the level of the forebrain migrated ventrally in a contiguous stream through the mesenchyme between the eye and the diencephalon. In the region of the midbrain, the cells migrated ventrolaterally as dispersed cells through the mesenchyme bordered by the lateral surface of the mesencephalon and the ectoderm. At the level of the hindbrain, neural crest cells migrated ventrolaterally in three subectodermal streams that were segmentally distributed. Each stream extended from the dorsal portion of the neural tube into the distal portion of the adjacent branchial arch. The order in which cranial neural crest cells populate their derivatives was determined by labelling embryos at different stages of development. Cranial neural crest cells populated their derivatives in a ventral-to-dorsal order, similar to the pattern observed at trunk levels. In order to confirm and extend the findings obtained with exo utero embryos, DiI (1,1-dioctadecyl-3,3,3',3'-tetramethylindo-carbocyanine perchlorate) was applied focally to the neural folds of embryos, which were then cultured for 24 hours. Because the culture technique permitted increased control of the timing and location of the DiI injection, it was possible to determine the duration of cranial neural crest cell emigration from the neural tube. Cranial neural crest cell emigration from the neural folds was completed by the 11-somite stage in the region of the rostral hindbrain, the 14-somite stage in the regions of the midbrain and caudal hindbrain and not until the 16-somite stage in the region of the forebrain. At each level, the time between the earliest and latest neural crest cells to emigrate from the neural tube appeared to be 9 hours. (ABSTRACT TRUNCATED AT 400 WORDS)
Image analysis using multifractals

The efficacy of the multifractal spectrum as a tool for characterizing images has been studied. This spectrum has been computed for digitized images of the nucleus of human cervical cancer cells and it was observed that the entire spectrum is almost fully reproduced for a normal cell while only the right half (q<0) of the spectrum is reproduced for a cancerous cell. Cells in stages in between the two extremes show a shortening of the left half of the spectrum proportional to their condition. The extent of this shortening has been found to be sufficient to permit a classification between three classes of cells at varying distances from a basal cancerous layer-the superficial cells, the intermediate cells and the parabasal cells. This technique may be used for automatic screening of the population while also indicating the stage of malignancy.
Cyberculture Theorists: Manuel Castells and Donna Haraway

This book surveys a 'cluster' of works that seek to explore the cultures of cyberspace, the Internet and the information society. It introduces key ideas, and includes detailed discussion of the work of two key thinkers in this area, Manuel Castells and Donna Haraway, as well as outlining the development of cyberculture studies as a field. To do this, the book also explores selected 'moments' in this development, from the early 1990s, when cyberspace and cyberculture were only just beginning to come together as ideas, up to the present day, when the field of cyberculture studies has grown and bloomed, producing innovative theoretical and empirical work from a diversity of standpoints. Key topics include: * life on the screen * network society * space of flows * cyborg methods. Cyberculture Theorists is the ideal starting point for anyone wanting to understand how to theorise cyberculture in all its myriad forms.
Lossy Color Image Compression Based on Singular Value Decomposition and GNU GZIP

In matrix algebra, the Singular value decomposition (SVD) is an factorization of complex matrix that has been applied to principal component analysis, canonical correlation in statistics, the determination of the low rank approximation of matrices. In this paper, using the SVD and the theory of low rank approximation of a matrix, we offer a new scheme for color image compression based on singular value decomposition and gzip. The scheme focuses on color images, thus fitting various network multimedia applications. SVD is applied to color image for low rank approximation. This compression scheme may have applications in sound and video compression.GNU zip is a compression utility designed to be a replacement for compress. Its main advantages over compress are much better compression and freedom from patented algorithms. The aim is to improve a fast procedure of computation and simple implementation of the algorithm. The performance of the new compression based on SVD and GNU GZIP is examined.
Θ-adaptive Neural Networks: a New Approach to Parameter Estimation

A novel use of neural networks for parameter estimation in nonlinear systems is proposed. The approximating ability of the neural network is used to identify the relation between system variables and parameters of a dynamic system. Two different algorithms, a block estimation method and a recursive estimation method, are proposed. The block estimation method consists of the training of a neural network to approximate the mapping between the system response and the system parameters which in turn is used to identify the parameters of the nonlinear system. In the second method, the neural network is used to determine a recursive algorithm to update the parameter estimate. Both methods are useful for parameter estimation in systems where either the structure of the nonlinearities present are unknown or when the parameters occur nonlinearly. Analytical conditions under which successful estimation can be carried but and several illustrative examples verifying the behavior of the algorithms through simulations are presented.
Utility based node selection scheme for cooperative localization

Cooperative positioning is a popular approach for indoor localization, which significantly outperforms conventional positioning techniques. This paper presents a method for iterative node localization, with a relatively small number of initial anchor nodes. In order to reduce error propagation in iterative schemes, it is necessary to use only reliable information among nodes. We introduce a reference node selection strategy based on utility functions. The method is completely distributed and involves only information from local neighborhood. Compared to the nearest neighbor and random node selection scheme, simulation results show that selecting subsets of nodes with highest utility values leads to more accurate position estimates.
Dual carrier multiplexed M-QAM signals modulated optical SSB transmission over fiber

In this paper, a simple hybrid radio-over fiber network that can transmit a different quadrature amplitude modulated (QAM) signals simultaneously over a 50-km fiber link is investigated. Simulation results indicate that a maximum of 162 Mbps-64QAM and 108 Mbps-16QAM signals can be sent simultaneously using Optical Single Side Band (OSSB) generation technique without any dispersion compensation. The proposed dual carrier multiplexing (DCM) architecture may be used for the multi-service transmission in heterogeneous networks. In presence of centralized control and processing, subcarrier multiplexing (SCM) techniques over fiber may provide economical and protocol-transparent solutions for the next-generation hybrid networks.
Simba: tunable end-to-end data consistency for mobile apps

Developers of cloud-connected mobile apps need to ensure the consistency of application and user data across multiple devices. Mobile apps demand different choices of distributed data consistency under a variety of usage scenarios. The apps also need to gracefully handle intermittent connectivity and disconnections, limited bandwidth, and client and server failures. The data model of the apps can also be complex, spanning inter-dependent structured and unstructured data, and needs to be atomically stored and updated locally, on the cloud, and on other mobile devices. In this paper we study several popular apps and find that many exhibit undesirable behavior under concurrent use due to inadequate treatment of data consistency. Motivated by the shortcomings, we propose a novel data abstraction, called a sTable, that unifies a tabular and object data model, and allows apps to choose from a set of distributed consistency schemes; mobile apps written to this abstraction can effortlessly sync data with the cloud and other mobile devices while benefiting from end-to-end data consistency. We build Simba, a data-sync service, to demonstrate the utility and practicality of our proposed abstraction, and evaluate it both by writing new apps and porting existing inconsistent apps to make them consistent. Experimental results show that Simba performs well with respect to sync latency, bandwidth consumption, server throughput, and scales for both the number of users and the amount of data.
Rollover Warning for Articulated Heavy Vehicles Based on a Time-to-Rollover Metric

A Time-To-Rollover (TTR) metric is proposed as the basis to assess rollover threat for an articulated heavy vehicle. The TTR metric accurately “counts-down” toward rollover regardless of vehicle speed and steering patterns, so that the level of rollover threat is accurately presented. There are two conflicting requirements in the implementation of TTR. On the one hand, a super-real-time model is needed. On the other hand, the TTR predicted by this model needs to be accurate enough under all driving scenarios. An innovative approach is proposed in this paper to solve this dilemma and the design process is illustrated in an example. First, a simple yet reasonably accurate yaw/roll model is identified. A Neural Network (NN) is then developed to mitigate the accuracy problem of this simple model. The NN takes the TTR generated by the simple model, vehicle roll angle, and change of roll angle to generate an enhanced NN-TTR index. The NN was trained and verified under a variety of driving patterns. It was found that an accurate TTR is achieved across all the driving scenarios we tested.
Evaluation of Zoll Medical’s ResQCPR System for cardiopulmonary resuscitation

Cardiac arrest remains a leading cause of death, currently affecting more than 250,000 Americans annually. As recommended by the American Heart Association, the current standard of care for patients with an out-of-hospital cardiac arrest (OHCA) includes manual cardiopulmonary resuscitation (S-CPR). Survival with favorable neurological function for all patients following OHCA and treated with S-CPR averages <6%. The ResQCPR System is intended to provide greater circulation to the heart and brain compared with S-CPR, thereby increasing the likelihood of survival. A recent Phase III, multicenter randomized study demonstrated a 50% increase in survival to hospital discharge with favorable neurologic function in subjects with an OHCA of presumed cardiac etiology treated with the ResQCPR System compared with conventional CPR. The ResQCPR System has been recently approved by the FDA as a CPR adjunct to improve the likelihood of survival in adult patients with non-traumatic cardiac arrest.
Entropy-based Pruning of Backoff Language Models

A criterion for pruning parameters from N-gram backoff language models is developed, based on the relative entropy between the original and the pruned model. It is shown that the relative entropy resulting from pruning a single N-gram can be computed exactly and efficiently for backoff models. The relative entropy measure can be expressed as a relative change in training set perplexity. This leads to a simple pruning criterion whereby all N-grams that change perplexity by less than a threshold are removed from the model. Experiments show that a production-quality Hub4 LM can be reduced to 26% its original size without increasing recognition error. We also compare the approach to a heuristic pruning criterion by Seymore and Rosenfeld (1996), and show that their approach can be interpreted as an approximation to the relative entropy criterion. Experimentally, both approaches select similar sets of N-grams (about 85% overlap), with the exact relative entropy criterion giving marginally better performance.
Max-affine estimators for convex stochastic programming

In this paper, we consider two sequential decision making problems with a convexity structure, namely an energy storage optimization task and a multi-product assembly example. We formulate these problems in the stochastic programming framework and discuss an approximate dynamic programming technique for their solutions. As the cost-to-go functions are convex in these cases, we use max-affine estimates for their approximations. To train such a max-affine estimate, we provide a new convex regression algorithm, and evaluate it empirically for these planning scenarios.
An adaptive blind SIMO identification approach to joint multichannel time delay estimation

Time delay estimation (TDE) is a difficult problem in a reverberant environment and the traditional generalized cross-correlation (GCC) methods perform poorly. The adaptive eigenvalue decomposition (AED) algorithm recently proposed by the authors exploits a blind channel identification (BCI) technique and deals with room reverberation more effectively. The AED algorithm was developed for a two-channel system. It requires that the two channels do not share any common zeros (a necessary condition of system identifiability). We generalize the AED algorithm to multichannel (more than 2) systems. Compared to the AED algorithm, the generalized method is more robust since it is less likely for all channels to share a common zero when more sensors are used.
Alternating Direction Method of Multipliers for Nonparallel Support Vector Machines

Recently, a novel nonparallel support vector machine (NPSVM) is proposed by Tian et al, which has several attracting advantages over its predecessors. A sequential minimal optimization algorithm(SMO) has already been provided to solve the dual form of NPSVM. Different from the existing work, we present a new strategy to solve the primal form of NPSVM in this paper. Our algorithm is designed in the framework of the alternating direction method of multipliers (ADMM), which is well suited to distributed convex optimization. Although the closed-form solution of each step can be written out directly, in order to be able to handle problems with a very large number of features or training examples, we propose to solve the underlying linear equation systems proximally by the conjugate gradient method. Experiments are carried out on several data sets. Numerical results indeed demonstrate the effectiveness of our method.