Miracle-dz/UniFAR-FTUs huggingface dataset:
Methodological issues in large-scale educational surveys

This thesis focuses on the application of item response theory (IRT) in the context of large scale international educational surveys like PISA 2009 (OECD). Although IRT methodology has been widely used in educational applications such as test construction, norming of examinations, detection of item bias and computerized adaptive testing, large scale surveys present a number of specific problems. A number of these problems are addressed in this thesis using PISA student data of the 2006 and 2009 cycles. The first problem in international comparative educational tests relates to the detection of cultural bias over countries. In this thesis, we targeted a problem know as country-specific Differential Item Functioning (CDIF) or country-by-item-interaction. This problem is tackled by modeling CDIF using country specific item parameters. In Chapter 2 this methodology is applied to the background questionnaires of the PISA 2009 cycle. In Chapter 3, this methodology is outlined and applied to the Reading dataset of the PISA 2006 cycle. The results showed that the impact of CDIF on the ranking of countries is not prominent and becomes almost negligible when the statistical uncertainty regarding the country means is properly taken into account. In Chapter 4, the practical significance of modeling item bias on the background questionnaire scales was studied not only in terms of the ordering of countries on the respective scales but also its impact on the results of regression analyses with latent variables in survey research. The next topic relates to the combination of the results of IRT measurement models with multilevel structural models to relate cognitive outcomes to background variables. According to our findings, results obtained using separately estimated plausible values and latent covariates (as provided in large scale data sets like PISA) were comparable with the results obtained using a concurrent estimation with raw data for the various kinds of IRT measurement models embedded in the structural (regression) model. The last Chapter gives an example of an advanced latent regression model based on plausible value methodology that explores the relation between socio-economic status and reading achievement in PISA.
Efficient walking with optimization for a planar biped walker with a torso by hip actuators and springs

SUMMARY This paper focuses on the use of passive dynamics to achieve efficient walking with simple mechanisms. A torso is added to a biped walker; and hip actuators, instead of ankle actuators, are used. A numerical approach is used to find the optimal control trajectories. A comparison between the cost functions of simple feedback control and optimal control is presented. Next, springs are added to the biped walking model at the hip joints to demonstrate the advantage of hip springs in terms of energy cost and ground conditions. The comparison between the torque costs with and without hip springs indicates that hip springs reduce the torque cost, particularly at a high walking speed.
Measuring Mathematical Competences of Engineering Students at the Beginning of Their Studies

This article reports about our efforts to determine engineering students' competence in mathematics. Our research is embedded in a larger project, KoM@ING–Modeling and developing competence: Integrated IRT based and qualitative studies with a focus on mathematics and its usage in engineering studies, within the program Modeling and Measuring Competencies in Higher Education (KoKoHS). KoKoHS provides the umbrella organization of several research projects addressing the modeling and measuring of competences at the college level. KoM@ING aims to model the role of engineering students' mathematical competences for their studies from both a quantitative and a qualitative perspective. Here, we report the development of a large-scale instrument assessing engineering freshmen's competence in mathematics by applying Rasch analysis to determine measures for item difficulties and student abilities. Several analyses were performed to provide insights into the measures' reliability and validity. In particular, to examine cognitive validity, we scrutinized students' think-aloud protocols when solving the items to investigate their problem solving abilities as a proxy for item difficulty. Overall, we found first evidence that our instrument is suitable to assess engineering freshmen's competence in mathematics. This instrument may be helpful to conduct further research and to inform those concerned with college organization and policy.
Multisensory Emplaced Learning: Resituating Situated Learning in a Moving World

This article outlines the implications of a theory of “sensory-emplaced learning” for understanding the interrelationships between the embodied and environmental in learning processes. Understanding learning as multisensory and contingent within everyday place-events, this framework analytically describes how people establish themselves as “situated learners. ” This approach is demonstrated through three examples of how culturally constructed sensory categories offer routes to knowing about the multisensoriality of learning experiences. This approach, we suggest, offers new routes within practice-oriented educational theories for understanding how human bodies become situated and embedded in cultural, social, and material practices within constantly shifting place-events.
An Agent-Based Model of Information Diffusion Final Report

Understanding how information spreads throughout a population can help public health officials improve how they communicate with the public in emergency situations. In this project, I implement a fast, memory-efficient agent-based information diffusion model inspired by the Bass model. I compare my discrete-time simulation to a traditional differential-equation version of the Bass model. Finally, I test my model by seeing how well it describes the real-life spread of information through a Twitter network.
Neuronal Induction of the Immunoproteasome in Huntington's Disease

Huntington's disease (HD) inclusions are stained with anti-ubiquitin and anti-proteasome antibodies. This, together with proteasome activity studies on transfected cells, suggest that an impairment of the ubiquitin-proteasome system (UPS) may be key in HD pathogenesis. To test whether proteasome activity is impaired in vivo, we performed enzymatic assays for the three peptidase activities of the proteasome in brain extracts from the HD94 conditional mouse model of HD. We found no inhibition of any of the activities, suggesting that if UPS impairment happens in vivo, it is not at the level of the proteasome catalytic core. Intriguingly, the chymotrypsin- and trypsin-like activities increased selectively in the affected and aggregate-containing regions: cortex and striatum. Western blot analysis revealed no difference in total proteasome content whereas an increase in the interferon-inducible subunits of the immunoproteasome, LMP2 and LMP7, was observed. These subunits confer to the proteasome catalytic properties that are optimal for MHC-I peptide presentation. Immunohistochemistry in control mouse brain revealed LMP2 and LMP7 mainly in neurons. Accordingly, their increase in HD94 mice predominantly took place in neurons, and 5% of the ubiquitin-positive cortical aggregates were also LMP2-positive. Ultrastructural analysis of neurons with high level of immunoproteasome subunits revealed signs of neurodegeneration like nuclear indentation or fragmentation and dark cell appearance. The neuronal induction of LMP2 and LMP7 and the associated signs of neurodegeneration were also found in HD postmortem brains. Our results indicate that LMP2 and LMP7 participate in normal neuronal physiology and suggest a role in HD neurodegeneration.
Population Scalability Analysis of Abstract Population-based Random Search: Spectral Radius

Population-based Random Search (RS) algorithms, such as Evolutionary Algorithms (EAs), Ant Colony Optimization (ACO), Artificial Immune Systems (AIS) and Particle Swarm Optimization (PSO), have been widely applied to solving discrete optimization problems. A common belief in this area is that the performance of a population-based RS algorithm may improve if increasing its population size. The term of population scalability is used to describe the relationship between the performance of RS algorithms and their population size. Although understanding population scalability is important to design efficient RS algorithms, there exist few theoretical results about population scalability so far. Among those limited results, most of them belong to case studies, e.g. simple RS algorithms for simple problems. Different from them, the paper aims at providing a general study. A large family of RS algorithms, called ARS, has been investigated in the paper. The main contribution of this paper is to introduce a novel approach based on the fundamental matrix for analyzing population scalability. The performance of ARS is measured by a new index: spectral radius of the fundamental matrix. Through analyzing fundamental matrix associated with ARS, several general results have been proven: (1) increasing population size may increase population scalability; (2) no super linear scalability is available on any regular monotonic fitness landscape; (3) potential super linear scalability may exist on deceptive fitness landscapes; (4) “bridgeable point” and “diversity preservation” are two necessary conditions for super linear scalability
Arabic Native Language Identification

In this paper we present the first application of Native Language Identification (NLI) to Arabic learner data. NLI, the task of predicting a writer’s first language from their writing in other languages has been mostly investigated with English data, but is now expanding to other languages. We use L2 texts from the newly released Arabic Learner Corpus and with a combination of three syntactic features (CFG production rules, Arabic function words and Part-of-Speech n-grams), we demonstrate that they are useful for this task. Our system achieves an accuracy of 41% against a baseline of 23%, providing the first evidence for classifier-based detection of language transfer effects in L2 Arabic. Such methods can be useful for studying language transfer, developing teaching materials tailored to students’ native language and forensic linguistics. Future directions are discussed.
Automatic syllable-based phoneme recognition using ESTER corpus

This paper presents an evaluation of speaker-independent continuous phoneme recognition systems on the French speech database ESTER. The tested systems are syllable-based phoneme recognizers, i.e. they use syllables as basic units together with syllabic bigram language models and HMM topologies adapted to syllables. Once identified, syllables are converted back to phones. In a previous paper, we introduced the transitory models in order to build a system for phonetic transcription guided by syllables that achieved a Phoneme Error Rate of 15.8% when tested on a small part of the French Bref80 corpus [6]. In this study, the transitory model system was tested on a more comprehensive set of test data, ESTER [3]. It was also compared to three other syllable-based systems relying respectively on monophones, on triphones with full cross syllable context expansion and on triphones with simple syllable internal context expansion. The results confirm the benefit of using syllables for phoneme identification, as well as the interest in using the transitory models in terms of complexity and processing speed compared to systems based on context-dependent models.
Clinical utility of the MMPI research scales in the assessment of adolescent acting out behaviors

The substantial number of Minnesota Multiphasic Personality Inventory (MMPI) research scales developed during the last 50 years provides a unique opportunity to compare the practical value of a large number of related scales and the methodologies used to develop them. To this end, the concurrent validity of 93 MMPI scales as measures of acting out (aggressiveness, chemical dependence, delinquency, family conflict, and school behavior problems) was evaluated in a sample of 327 adolescent inpatients
No structural cerebral differences between children with a history of bacterial meningitis and healthy siblings

Aim: After bacterial meningitis, about one‐third of children develops academic and/or behavioural limitations. The aim of our study was to search for structural differences in the brain, with a special focus on the hippocampus, between childhood survivors of bacterial meningitis with and without academic and/or behavioural limitations and healthy siblings.
Management Women and the New Facts of Life

The recognition that an increasing proportion of the workforce over the next decade will be women has important implications for corporate employers, not least in the area of leadership positions. Two types of women in organisations are defined – career‐primary and career‐and‐family – and it is asserted that if organisations are to retain their competitive advantage they must recognise the value of both types and provide a more flexible work environment. This will benefit both the individual and the employer. The individual will have freedom of choice between career or family, or a combination of both; and the employer will be able to retain a valuable resource – the talented women executives.
Context-sensitive text mining and belief revision for intelligent information retrieval on the web

Autonomous information agents alleviate the information overload problem on the Internet. The AGM belief revision framework provides a rigorous formal foundation to develop adaptive information agents. The expressive power of the belief revision logic allows information seekers' changing information preferences and the underlying retrieval contexts to be captured in information agents. By exploiting the relevant retrieval contexts, information agents can proactively recommend interesting information items to their users. Contextual knowledge for information retrieval can be acquired by information agents via context-sensitive text mining. The induction power brought by context-sensitive text mining and the nonmonotonic reasoning capability offered by a belief revision system are complementary to each other. This paper illustrates a novel approach of integrating the proposed text mining method into the belief revision based adaptive information agents to improve the agents' learning autonomy and prediction power. Our initial experiments show that the symbolic adaptive information agents outperform their vector space model based counterparts.
Distributed Biased Min-Consensus With Applications to Shortest Path Planning

Distributed consensus has been intensively studied in recent years as a means to mitigate state differences among dynamic nodes on a graph. It has been successfully employed in various applications, e.g., formation control of multirobots, load balancing, and clock synchronization. However, almost all the existing applications cast an impression of consensus as a simple process to iteratively reach agreement, without any clue on possibility to generate advanced complexity, say shortest path planning, which has been proved to be NP-hard. Counterintuitively, we show for the first time that the complexity of shortest path planning can emerge from a perturbed version of a min-consensus protocol, which as a case study may shed lights to researchers in the field of distributed control to rethink the nature of complexity and the distance between control and intelligence. Besides, we rigorously prove the convergence of graph dynamics and its equivalence to shortest path solutions. An illustrative simulation on a small-scale graph is provided to show the convergence of the biased min-consensus dynamics to shortest path solution over the graph. To demonstrate the scalability to large-scale problems, a graph with 43 826 nodes, which corresponds to a map of a maze in 2-D, is considered in the simulation study. Apart from possible applications in robot path planning, the result is further extended to robot complete coverage, showing its potential in real practice such as cleaning robots.
The effects of partial observability when building fully correlated maps

This paper presents an analysis of the fully correlated approach to the simultaneous localization and map building (SLAM) problem from a control systems theory point of view, both for linear and nonlinear vehicle models. We show how partial observability hinders full reconstructibility of the state space, making the final map estimate dependent on the initial observations. Nevertheless, marginal filter stability guarantees convergence of the state error covariance to a positive semidefinite covariance matrix. By characterizing the form of the total Fisher information, we are able to determine the unobservable state space directions. Moreover, we give a closed-form expression that links the amount of reconstruction error to the number of landmarks used. The analysis allows the formulation of measurement models that make SLAM observable.
A framework for interpreting distinct patterns of hemispatial neglect

We report analyses of the performance of three stroke patients which reveal distinct patterns of spatially specific errors in reading and other perceptual tasks. These patterns of performance can be explained by proposing hemispatial neglect in different spatial coordinates in each case. Results from reading and perceptual tasks by two patients with right parietal damage, BPN and JOD (whose results have not been previously reported) are contrasted to those from a previously reported patient, NG. These contrasting patterns of errors provide support for a model of word and visual object recognltlon which assumes that these processes involve computing (at least) three distinct levels of representation with different coordinate frames. The data we report also provide evidence that hemispatial neglect can result from selective damage to any of these levels of representation - i.e. damage to a retinocentric, a stimulus-centered, or an object-centered level of representation. We show that this model provides a c.
A Risky Venture: Income Dynamics within the Non-Corporate Private Business Sector

This paper represents the first attempt in the literature to examine the properties and document the empirical stylized facts of income risk from privately held businesses in the US using a large panel. We employ a new, large, and confidential panel of US income tax returns for the period 1987-2009 to extensively characterize and quantify business income risks and to compare to labor income or earnings risks. We find that business income is much riskier than labor income, even conditional on a households continued participation in private business endeavors. Compared to labor income, business income is less persistent, and it is characterized by higher probabilities of extreme upward or downward mobility. Our data also suggest that high-income households are more likely to face the tail risks on both ends of the business income distribution. Furthermore, we find that, as compared to labor income, the cross-sectional variation in business income is less explained by heterogeneity across households than within household income variation. Finally, we use our models of income dynamics models to calibrate a life-cycle model with endogenous business ownership to explain an apparent business ownership puzzle. ∗Jason DeBacker, Middle Tennessee State University, jason.debacker@mtsu.edu; Vasia Panousi, Federal Reserve Board, vasia.panousi@frb.gov; Shanthi Ramnath, US Department of the Treasury, shanthi.ramnath@treasury.gov. We are grateful to Joe Altonji, George-Marios Angeletos, Chris Carroll, Janice Eberly, Glenn Follette, John Haltiwanger, Marco Cagetti, Mariacristina De Nardi, Kevin Moore, Ricardo Reis, Jose-Victor Rios-Rull, John Sabelhaus, Louise Sheiner, Paul Smith, Gianluca Violante, Annette Vissing-Jørgensen, and the seminar participants at the Federal Reserve Board, the 6th annual meeting of the Portuguese Economic Journal, the Summer 2012 NBER Capital Markets Group (EFEL), the Summer 2012 NBER Conference on Research in Income and Wealth (CRIW), Indiana University, the Census Bureau, the Brookings Institute, the Bank of Portugal, and the New York Fed for helpful comments and discussions. We are thankful to our discussants, Nikolai Roussanov (EFEL) and Ron S. Jarmin (CRIW), for extremely insightful suggestions. The views in this paper are solely those of the authors and do not necessarily represent those of the Treasury Department, the Board of Governors of the Federal Reserve System, or members of their staffs.
Successful Establishment of an Orthotopic Hepatoblastoma In Vivo Model in NOD/LtSz-scid IL2Rγnull Mice

Investigation of hepatoblastoma in experimental conditions contributes relevantly to a detailed understanding of tumor biology and the investigation of new treatment approaches. Most systematical analyses currently use subcutaneous xenografts. We established a reproducible intrahepatic model with the hepatoblastoma-cell lines HuH6 and HepT1. The cells were stably transfected with a plasmid vector encoding for Gaussia luciferase. HuH6 and HepT1 were injected intrasplenically in NOD/LtSz-scid IL2Rγnull mice. Mice were splenectomized in order to avoid intrasplenical tumor growth. Multifocal intrahepatic tumor growth was observed in 85% (11/13) of HuH6 tumors and 55% (5/9) of HepT1 tumors. Serum Alpha-fetoprotein and Gaussia luciferase increased 5 weeks after tumor-cell inoculation. Tumors were detected by MRI at this time point. Immunhistochemical analysis such as vascularity (CD31), proliferation index (Ki-67), cytokeratin 7 and distribution of β -catenin in intrahepatic tumors were different to subcutaneous tumors. We established a reproducible xenograft model for intrahepatic hepatoblastoma growth with a high tumor incidence. Monitoring of tumor cell viability was optimized by measuring GLuc. This model enables further experimental investigations of HB in a more physiological milieu as emphasized by the β-catenin distribution.
Relapsing, Running, and Relieving: A Model for High-Risk Behavior in Recovery

&NA; This research paper reports on a qualitative, grounded theory study of 30 women's progress toward recovery. It identifies processes of "relapsing," "running," and "relieving" and organizes them into an integrated model for high-risk behavior for women in recovery. The model explores high-risk behavior through the dimensions of internality and externality and finds that the process of recovery for women is not linear. The development of this model is important in helping women at various points in the recovery process; alerting these women to issues that they may not completely understand; and giving treatment professionals a strengths-based perspective from which to examine this phenomenon.
A Fair Data Transmission Strategy in Underwater Acoustic Sensor Networks

When Underwater Acoustic Sensor Networks (UASNs) transmit large amounts of data, the collisions of link layer increase, and it causes that the traffic load of link is heavier. These situations could easily lead to network data loss, unfair channel competition and unfair data transmis - sion. In order to reduce collisions and improve the reliability and fairness of data transmission, Time Distance Ratio (TDR) and underwater fairness transmission are presented, and this paper proposes a Fair Data Transmission Strategy (FDTS) for UASNs, which consists of queue management, transmission control and competition waiting. It focuses on considering the fairness of data transmission and enhancing network performance in case of network congestion. Simulation results show that when network load is heavier, FDTS not only effectively improve the fairness of data transmission and the success rate of delivery but also reduce end-to-end delay, average energy consumption and TDR of packets