Miracle-dz/UniFAR-FTUs huggingface dataset:
Presentation of a Complex Permittivity-Meter with Applications for Sensing the Moisture and Salinity of a Porous Media

This paper describes a sensor dedicated to measuring the vertical profile of the complex permittivity and the temperature of any medium in which sensor electrodes are inserted. Potential applications are the estimate of the humidity and salinity in a porous medium, such as a soil. It consists of vertically-stacked capacitors along two conductive parallel cylinders of 5 cm in diameter and at a 10-cm distance to scan a significant volume of the medium (∼1 L). It measures their admittances owing to a self-balanced impedance bridge operating at a frequency in the range of 1–20 MHz, possibly 30 MHz. Thanks to accurate design and electronic circuit theory-based modeling, the determination of the admittances takes into account all distortions due to lead and bridge electromagnetic effects inside the sensor when working at high frequencies. Calibration procedures and uncertainties are presented. The article also describes developments to make the present sensor autonomous on digital acquisition, basic data treatment and energy, as well as able to transfer stored data by a radio link. These steps in progress are prerequisites for a wireless network of sensors.
Inferring Users' Preferences from Crowdsourced Pairwise Comparisons: A Matrix Completion Approach

Inferring user preferences over a set of items is an important problem that has found numerous applications. This work focuses on the scenario where the explicit feature representation of items is unavailable, a setup that is similar to collaborative filtering. In order to learn a user's preferences from his/her response to only a small number of pairwise comparisons, we propose to leverage the pairwise comparisons made by many crowd users, a problem we refer to as crowdranking. The proposed crowdranking framework is based on the theory of matrix completion, and we present efficient algorithms for solving the related optimization problem. Our theoretical analysis shows that, on average, only O(r log m) pairwise queries are needed to accurately recover the ranking list of m items for the target user, where r is the rank of the unknown rating matrix, r << m. Our empirical study with two real-world benchmark datasets for collaborative filtering and one crowdranking dataset we collected via Amazon Mechanical Turk shows the promising performance of the proposed algorithm compared to the state-of-the-art approaches.
Online learning via sequential complexities

We consider the problem of sequential prediction and provide tools to study the minimax value of the associated game. Classical statistical learning theory provides several useful complexity measures to study learning with i.i. d. data. Our proposed sequential complexities can be seen as extensions of these measures to the sequential setting. The developed theory is shown to yield precise learning guarantees for the problem of sequential prediction. In particular, we show necessary and sufficient conditions for online learnability in the setting of supervised learning. Several examples show the utility of our framework: we can establish learnability without having to exhibit an explicit online learning algorithm.
High Dimensional Classification of Structural MRI Alzheimer’s Disease Data Based on Large Scale Regularization

In this work we use a large scale regularization approach based on penalized logistic regression to automatically classify structural MRI images (sMRI) according to cognitive status. Its performance is illustrated using sMRI data from the Alzheimer Disease Neuroimaging Initiative (ADNI) clinical database. We downloaded sMRI data from 98 subjects (49 cognitive normal and 49 patients) matched by age and sex from the ADNI website. Images were segmented and normalized using SPM8 and ANTS software packages. Classification was performed using GLMNET library implementation of penalized logistic regression based on coordinate-wise descent optimization techniques. To avoid optimistic estimates classification accuracy, sensitivity, and specificity were determined based on a combination of three-way split of the data with nested 10-fold cross-validations. One of the main features of this approach is that classification is performed based on large scale regularization. The methodology presented here was highly accurate, sensitive, and specific when automatically classifying sMRI images of cognitive normal subjects and Alzheimer disease (AD) patients. Higher levels of accuracy, sensitivity, and specificity were achieved for gray matter (GM) volume maps (85.7, 82.9, and 90%, respectively) compared to white matter volume maps (81.1, 80.6, and 82.5%, respectively). We found that GM and white matter tissues carry useful information for discriminating patients from cognitive normal subjects using sMRI brain data. Although we have demonstrated the efficacy of this voxel-wise classification method in discriminating cognitive normal subjects from AD patients, in principle it could be applied to any clinical population.
‘We were here first, so we determine the rules of the game’: Autochthony and prejudice towards out‐groups

This paper identifies autochthony—the belief that a place belongs to its original inhabitants and that they are therefore more entitled—as a relevant new determinant of out-group prejudice. We hypothesized that autochthony uniquely predicts prejudice towards migrant groups and that it mediates the relationship between national identification and prejudice. The mediation process was anticipated to be especially strong for people who perceive out-group encroachment, that is, those who feel that immigrants are ‘getting out of place’. These hypotheses were tested in two studies using nationally representative samples of native Dutch participants. In Study 1 (N = 793), we showed that autochthony is an empirically distinct construct and that it is a unique predictor of prejudice. Furthermore, higher national identifiers expressed stronger claims of autochthony, and these claims were in turn associated with more negative feelings towards migrant groups. Study 2 (N = 466) showed support for a moderated mediation model: Beliefs in autochthony were only related to prejudice for participants who perceived out-group encroachment. Copyright © 2013 John Wiley & Sons, Ltd.
Inclusion and Problem-Based Learning: Roles of Students in a Mixed-Ability Group

Abstract The literature on the use of problem-based learning in K–12 settings has traditionally focused on gifted and average students. However, mainstreaming is placing increasing numbers of students with special needs in general education classrooms. This case study examined how members of a small group in a mainstreamed seventh grade science class interacted with and supported each other as they engaged in a problem-based learning (PBL) unit. The group included one mainstreamed and two average students. We used conversation analysis and coding to analyze interview and video data of all 10 class sessions. Results indicated that each group member filled a unique role—group manager, task guidance provider, and task performer—and helped each other overcome individual difficulties. Results suggest that mainstreamed groups have the potential to effectively engage in PBL, and that PBL may increase the motivation and social confidence of students with special needs. We suggest types of scaffolds that could support mainstreamed students during PBL units.
On Learning and Representations in Cognitive Architectures for HRI

The goal of research in the field of cognitive architecture is to create general intelligent behavior. In contrast, the state-ofthe-art in the field of Human-Robot-Interaction is to handcraft systems that are specially tailored to certain scenarios, thus creating specific rather than general solutions. In this paper I subscribe to the view that we need to understand how robots can learn to interact, rather than to try to hardcode general intelligent social behavior. I further describe new trends in the field on how to implement a knowledge representation for a robot and provide further ideas of how such a knowledge representation could be learned.
Analysis of the multitrait^multimethod matrix by confirmatory factor analysis

The multitrait-multimethod (MTMM) matrix permits examination of the convergent and discriminant validity of psychological measures. Estimation using confirmatory factor analysis (CFA), the predominant analytical technique, has often resulted in severe difficulties, such as out-of-range estimates and convergence problems. This article shows that an important special case of one of the more frequently advocated CFA models is not identified and is therefore not estimable. Because most MTMM data are likely to conform closely to this special case, resulting analyses suffer from empirical underidentification. Alternaive CFA models are discussed
Understanding the Limits of Capacity Sharing in CMP Private Caches

Chip Multi Processor (CMP) systems present interesting design challenges at the lower levels of the cache hierarchy. Private L2 caches allow easier processor-cache design reuse, thus scaling better than a system with a shared L2 cache, while offering better performance isolation and lower access latency. While some private cache management schemes that utilize space in peer private L2 caches have been recently proposed, we find that there is significant potential for improving their performance. We propose and study an oracular scheme, OPT, which identifies the performance limits of schemes to manage private caches. OPT uses offline-generated traces of cache accesses to uncover applications’ reuse patterns. OPT uses this perfect knowledge of each application’s future memory accesses to optimally place cache blocks brought on-chip in either the local or a remote private L2 cache. We discover that in order to optimally manage private caches, peer private caches must be utilized not only at a local-cache-replacement time, as has been previously proposed, but also at cache-placement time it may be better to place a missed block directly into a peer L2 rather than the traditional approach of first bringing it into the local L2. We implement OPT on a 4-core CMP with 512KB, 8-way, private caches, across 10 carefully chosen, relevant, multiprogram workload mixes. We find that compared to a baseline system that does not employ capacity sharing across private caches, OPT improves weighted-speedup (performance metric) by 13.4% on average. Further, compared to the state of the art technique for private cache management, OPT improves weighted-speedup by 11.2%. This shows the significant potential that exists for improving previously proposed private cache management schemes.
Child marriage or forced marriage? South Asian communities in North East England

This article addresses the links between child marriage and forced marriage in the UK, drawing from a research study on South Asian communities in North East England. It looks at definitional issues through an analysis of UK and South Asian policies. It also analyses how these concepts are understood by service providers, survivors of child marriage and young people from South Asian communities. Finally, concepts such as gender, age, familial and community control reflected in normative marriage practices are addressed.
Parametric failures in CMOS ICs - a defect-based analysis

Defect-based test studies have thoroughly characterized CMOS IC hard bridge and open defects while less is known about a third class called parametric failures. These are more difficult to detect, and their presence is growing in CMOS IC nanoelectronics. The objective of this work is to present data that encompass the electronic properties of parametric failures that affect our ability to test present and future CMOS ICs. While parametric failures are widely reported, we seek to classify these failures with supporting data. Solutions to this complex test problem require that we structure and formalize their behaviors. Data indicate that multiparameter test strategies have the best match to some of the failures while good test strategies do not exist for others.
Effect of Gaze Position and Blur on Stepping Accuracy in Older Adults

Purpose To examine the effects of gaze position and optical blur, similar to that used in multifocal corrections, on stepping accuracy for a precision stepping task among older adults. Methods Nineteen healthy older adults (mean age, 71.6 ± 8.8 years) with normal vision performed a series of precision stepping tasks onto a fixed target. The stepping tasks were performed using a repeated-measures design for three gaze positions (fixating on the stepping target as well as 30 and 60 cm farther forward of the stepping target) and two visual conditions (best-corrected vision and with +2.50DS blur). Participants’ gaze position was tracked using a head-mounted eye tracker. Absolute, anteroposterior, and mediolateral foot placement errors and within-subject foot placement variability were calculated from the locations of foot and floor-mounted retroreflective markers captured by flash photography of the final foot position. Results Participants made significantly larger absolute and anteroposterior foot placement errors and exhibited greater foot placement variability when their gaze was directed farther forward of the stepping target. Blur led to significantly increased absolute and anteroposterior foot placement errors and increased foot placement variability. Furthermore, blur differentially increased the absolute and anteroposterior foot placement errors and variability when gaze was directed 60 cm farther forward of the stepping target. Conclusions Increasing gaze position farther ahead from stepping locations and the presence of blur negatively impact the stepping accuracy of older adults. These findings indicate that blur, similar to that used in multifocal corrections, has the potential to increase the risk of trips and falls among older populations when negotiating challenging environments where precision stepping is required, particularly as gaze is directed farther ahead from stepping locations when walking.
Novel superparamagnetic Core(Shell) nanoparticles for magnetic targeted drug delivery and hyperthermia treatment

Superparamagnetic /spl gamma/-Fe/sub 2/O/sub 3/(Au) core(shell) nanoparticles with narrow size distribution and different shell thicknesses are synthesized. On conjugation with serum albumin, this set of particles forms an ideal candidate to be used in magnetic targeted drug delivery and hyperthermia treatment. In this contribution, the synthesis and characterizations of their size, optical, and magnetic properties are reported. It has also been shown that using different shell thicknesses, it is possible to tune not only the plasmon response of the gold shell but also the magnetic property of these superparamagnetic nanoparticles.
Exploration of the (Non-)Asymptotic Bias and Variance of Stochastic Gradient Langevin Dynamics

Applying standard Markov chain Monte Carlo (MCMC) algorithms to large data sets is computationally infeasible. The recently proposed stochastic gradient Langevin dynamics (SGLD) method circumvents this problem in three ways: it generates proposed moves using only a subset of the data, it skips the Metropolis-Hastings accept-reject step, and it uses sequences of decreasing step sizes. In Teh et al. (2014), we provided the mathematical foundations for the decreasing step size SGLD, including consistency and a central limit theorem. However, in practice the SGLD is run for a relatively small number of iterations, and its step size is not decreased to zero. The present article investigates the behaviour of the SGLD with fixed step size. In particular we characterise the asymptotic bias explicitly, along with its dependence on the step size and the variance of the stochastic gradient. On that basis a modified SGLD which removes the asymptotic bias due to the variance of the stochastic gradients up to first order in the step size is derived. Moreover, we are able to obtain bounds on the finite-time bias, variance and mean squared error (MSE). The theory is illustrated with a Gaussian toy model for which the bias and the MSE for the estimation of moments can be obtained explicitly. For this toy model we study the gain of the SGLD over the standard Euler method in the limit of large data sets.
Non-intrusive Tracing in the Internet

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Maneuvering Together to Develop New Practices: Examining Our Collaborative Processes

Although collaboration is foundational to self-study methodology, few self-studies of teacher education practices have focused on collaborative teaching processes. In this inquiry, two teacher educators report a two-year self-study into the collaborative processes of planning and reflection that they used to co-construct significant changes to their practices in concurrent sections of a writing methods course. Situated in narrative inquiry, cyclical reflective and dialogic methods were used to generate and analyze data. Examination of our collaborative processes revealed that four interlinked factors were central to the ways in which we worked together: our modus operandi, shifting roles, collective vision, and support for risk-taking. We discuss these factors and how we maneuvered together to develop new practices; we also suggest implications for teacher educators and self-study researchers.
Knowledgeable development environments using shared design models

We describe MASTERMIND, a step toward our vision of a knowledge-based design-time and run-time environment where human-computer interfaces development is centered around an all-encompassing design model. The MASTERMIND approach is intended to provide integration and continuity across the entire life cycle of the user interface. In addition it facilitates higher quality work within each phase of the life cycle. MASTERMIND is an open framework, in which the design knowledge base allows multiple tools to come into play and makes knowledge created by each tool accessible to the others.
Utilizing Dynamics and Reward Models in Learning Strategy Fusion

Learning strategy (LS) fusion is our previous work in reinforcement learning (RL) framework. LS fusion fuses multiple LSs, such as transfer learning, for a single task of a robot. This paper introduces two LSs into LS fusion: an LS to learn a dynamics and a reward models, and an LS using a model-based RL method. Especially, we propose to use the MixFS dynamics model which is also our previous work. MixFS decomposes the dynamics model into the task specific elements and the task invariant elements. Thus, we can initialize the dynamics model of a task by transferring the one of the other task. In simulation experiments, we apply LS fusion with the new LSs to maze tasks of a small humanoid robot, where the primitive motions, crawling and turning, are also pre-learned by LS fusion. The results demonstrate that the new LSs improve the learning speed by using MixFS.
Appropriate Sleep Duration and Physical Activity Modulate Cognitive Improvement

Sleep and brain work closely and are dependent on each other. Physical exercise is now known as an efficient anti depressive for both healthy brain and cognitive decline. But few specific studies show relation between combination of these two factors and the probability to develop, when people are aged less than forty years old, cognitive impairments such as mnesic pathology, memory encoding and retrieval of information. The aim of this study is to explore how cognitive impairments are linked to sleep duration and physical exercise; and if this difference is related to sociodemographic items that we choose. This case control epidemiological study was led by a questionnaire incorporating the shorter version of McNair Test, usually employed to detect cognitive complains. The questionnaire also included ten socio-demographic items and twenty-five other questions divided in three sections: the quality of sleep, levels of physical exercise and smoking (not investigated in the present study). We recruited one hundred participants through universities in Montreal, Canada. Our results demonstrated that the stress levels are comparable in men and women. Specifically the results showed that lack of sleep combined with insufficient physical activity radically affects the cognitive score for memory, attention and MLD. The McNair score indicated that the cognitive performance decreased for all these parameters. Physical exercise and good quality of sleep including an appropriate duration, contribute both to a better cognition performance for young adults (between 18 until 40 years old) until midlife.
A Robust Alternating Direction Method for Constrained Hybrid Variational Deblurring Model

In this work, a new constrained hybrid variational deblurring model is developed by combining the non-convex first- and second-order total variation regularizers. Moreover, a box constraint is imposed on the proposed model to guarantee high deblurring performance. The developed constrained hybrid variational model could achieve a good balance between preserving image details and alleviating ringing artifacts. In what follows, we present the corresponding numerical solution by employing an iteratively reweighted algorithm based on alternating direction method of multipliers. The experimental results demonstrate the superior performance of the proposed method in terms of quantitative and qualitative image quality assessments.
Hunting and Illegal Violence Against Humans and Other Animals: Exploring the Relationship

This study examined the relationship between hunting and illegal violence among college males. Although similar on many socio-demographic characteristics such as age and social class (parents' education and occupation), hunters were more likely than non-hunters to be white and Protestant. They also were more likely to have grown up with a family member who hunted. Hunters were about twice as likely to have been violent toward nonhuman animals; however, one type of violence—killing wild or stray animals—accounted for this difference. Regarding violence toward people, hunters were more than twice as likely to have damaged or destroyed private or public property during their last year in high school but were no more likely during that year to have fought with other persons. Thus, at least for this sample, hunting related to harming animals in the wild and to property damage but not to other forms of animal abuse or violence against humans. This paper discusses possible explanations for this result and limitations of the study.