Miracle-dz/UniFAR-FTUs huggingface dataset:
"Doing" parenthood : fragile families in the fast life and under mass correctional supervision

Parenthood is a role that shapes the lives of parents and children. According to the sociology of families and marriages, criminology, and the sociology of punishment, the most alienated individuals in unequal America practice parenthood in fragile families struggling with poverty, the code of the street, and under correctional supervision. In attempts to connect and contribute to these literatures, this research project examines how individuals’ delinquent/criminal role performance on the street stage and client/inmate role performance on the correctional stage influence their parent role performance on the home stage. To do so, this qualitative study collected 57 semi-structured interviews (12 mothers and 45 fathers) and analyzes participants’ parent role, delinquent/criminal role, and client/inmate role. The findings suggest that a cross-generational role conflict shapes participants’ parent role performance throughout their life course. Although conflicting roles (roles with conflicting expectations) can coexist in the self, limited resources (time, energy, and money) and problematic boundaries (weak or impenetrable) between social situations bring role conflict to the center of role performance. In this case, the role conflict between participants’ ideal parent role on the home stage, delinquent/criminal role on the street stage, and client/inmate role on the correctional stage shapes participants’ parent role performance throughout their life course. Abstract Approved: _________________________________________________ Thesis Supervisor _________________________________________________ Title and Department _________________________________________________ Date _________________________________________________ Thesis Supervisor _________________________________________________ Title and Department _________________________________________________ DateApproved: _________________________________________________ Thesis Supervisor _________________________________________________ Title and Department _________________________________________________ Date _________________________________________________ Thesis Supervisor _________________________________________________ Title and Department _________________________________________________ Date “DOING” PARENTHOOD: FRAGILE FAMILIES IN THE FAST LIFE AND UNDER MASS CORRECTIONAL SUPERVISION by Ana Lilia Campos-Holland A thesis submitted in partial fulfillment of the requirements for the Doctor of Philosophy degree in Sociology in the Graduate College of the University of Iowa
Least-Squares Temporal Difference Learning

TD( ) is a popular family of algorithms for approximate policy evaluation in large MDPs. TD( ) works by incrementally updating the value function after each observed transition. It has two major drawbacks: it makes ine cient use of data, and it requires the user to manually tune a stepsize schedule for good performance. For the case of linear value function approximations and = 0, the Least-Squares TD (LSTD) algorithm of Bradtke and Barto (Bradtke and Barto, 1996) eliminates all stepsize parameters and improves data e ciency. This paper extends Bradtke and Barto's work in three signi cant ways. First, it presents a simpler derivation of the LSTD algorithm. Second, it generalizes from = 0 to arbitrary values of ; at the extreme of = 1, the resulting algorithm is shown to be a practical formulation of supervised linear regression. Third, it presents a novel, intuitive interpretation of LSTD as a model-based reinforcement learning technique.
Preliminary evidence for reduced posterror reaction time slowing in hyperactive/inattentive preschool children

Background: Attention deficit/hyperactivity disorder (ADHD) has been associated with deficits in self-regulatory cognitive processes, some of which are thought to lie at the heart of the disorder. Slowing of reaction times (RTs) for correct responses following errors made during decision tasks has been interpreted as an indication of intact self-regulatory functioning and has been shown to be attenuated in school-aged children with ADHD. This study attempted to examine whether ADHD symptoms are associated with an early-emerging deficit in posterror slowing. Method: A computerized two-choice RT task was administered to an ethnically diverse sample of preschool-aged children classified as either “control” (n = 120) or “hyperactive/inattentive” (HI; n = 148) using parent- and teacher-rated ADHD symptoms. Analyses were conducted to determine whether HI preschoolers exhibit a deficit in this self-regulatory ability. Results: HI children exhibited reduced posterror slowing relative to controls on the trials selected for analysis. Supplementary analyses indicated that this may have been due to a reduced proportion of trials following errors on which HI children slowed rather than due to a reduction in the absolute magnitude of slowing on all trials following errors. Conclusions: High levels of ADHD symptoms in preschoolers may be associated with a deficit in error processing as indicated by posterror slowing. The results of supplementary analyses suggest that this deficit is perhaps more a result of failures to perceive errors than of difficulties with executive control.
A study of the difficulties of novice programmers

Programming is related to several fields of technology, and many university students are studying the basics of it. Unfortunately, they often face difficulties already on the basic courses. This work studies the difficulties in learning programming in order to support developing learning materials for basic programming courses. The difficulties have to be recognized to be able to aid learning and teaching in an effective way.An international survey of opinions was organized for more than 500 students and teachers. This paper analyses the results of the survey. The survey provides information of the difficulties experienced and perceived when learning and teaching programming. The survey results also provide basis for recommendations for developing learning materials and approaches.
Building low-diameter P2P networks

In a peer-to-peer (P2P) network, nodes connect into an existing network and participate in providing and availing of services. There is no dichotomy between a central server and distributed clients. Current P2P networks (e.g., Gnutella) are constructed by participants following their own uncoordinated (and often whimsical) protocols; they consequently suffer from frequent network overload and fragmentation into disconnected pieces separated by choke-points with inadequate bandwidth. The authors propose a simple scheme for participants to build P2P networks in a distributed fashion, and prove that it results in connected networks of constant degree and logarithmic diameter. It does so with no global knowledge of all the nodes in the network. In the most common P2P application to date (search), these properties are important.
Conjunction error rates on a continuous recognition memory test: little evidence for recollection.

Two experiments examined conjunction memory errors on a continuous recognition task where the lag between parent words (e.g., blackmail, jailbird) and later conjunction lures (blackbird) was manipulated. In Experiment 1, contrary to expectations, the conjunction error rate was highest at the shortest lag (1 word) and decreased as the lag increased. In Experiment 2 the conjunction error rate increased significantly from a 0- to a 1-word lag, then decreased slightly from a 1- to a 5-word lag. The results provide mixed support for simple familiarity and dual-process accounts of recognition. Paradoxically, searching for an item in memory does not appear to be a good encoding task.
Well-rounded lattices for reliability and security in Rayleigh fading SISO channels

For many wiretap channel models asymptotically optimal coding schemes are known, but less effort has been put into actual realizations of wiretap codes for practical parameters. Bounds on the mutual information and error probability when using coset coding on a Rayleigh fading channel were recently established by Oggier and Belfiore, and the results in this paper build on their work. However, instead of using their ultimate inverse norm sum approximation, a more precise expression for the eavesdropper's probability of correct decision is used in order to determine a general class of good coset codes. The code constructions are based on well-rounded lattices arising from simple geometric criteria. In addition to new coset codes and simulation results, novel number-theoretic results on well-rounded ideal lattices are presented.
Relationship intimacy in software ecosystems: a survey of the Dutch software industry

Software vendors depend on suppliers to provide the underlying technology for domain specific solutions. As a consequence, software vendors cooperate with suppliers to deliver a product. This cooperation results in supplier dependence, but also leads to opportunities. We present the results of an exploratory research based on twenty-seven case studies, identifying supplier strategies and resulting trade-offs. Strategies range from fully depending on large software ecosystem orchestrators to a minimal dependency strategy. Furthermore, we identify factors at play when selecting suppliers for different components. These factors include; ecosystem health indicators, product and license type and intensive support and maintenance flows. The results presented in this paper can be used by software vendors to assess their software supply network to review supplier relationships, but also for future research.
Self-Organising Zooms for Decentralised Redundancy Management in Visual Sensor Networks

When visual sensor networks are composed of cameras which can adjust the zoom factor of their own lens, one must determine the optimal zoom levels for the cameras, for a given task. This gives rise to an important trade-off between the overlap of the different cameras' fields of view, providing redundancy, and image quality. In an object tracking task, having multiple cameras observe the same area allows for quicker recovery, when a camera fails. In contrast having narrow zooms allow for a higher pixel count on regions of interest, leading to increased tracking confidence. In this paper we propose an approach for the self-organisation of redundancy in a distributed visual sensor network, based on decentralised multi-objective online learning using only local information to approximate the global state. We explore the impact of different zoom levels on these trade-offs, when tasking Omni directional cameras, having perfect 360-degree view, with keeping track of a varying number of moving objects. We further show how employing decentralised reinforcement learning enables zoom configurations to be achieved dynamically at runtime according to an operator's preference for maximising either the proportion of objects tracked, confidence associated with tracking, or redundancy in expectation of camera failure. We show that explicitly taking account of the level of overlap, even based only on local knowledge, improves resilience when cameras fail. Our results illustrate the trade-off between maintaining high confidence and object coverage, and maintaining redundancy, in anticipation of future failure. Our approach provides a fully tunable decentralised method for the self-organisation of redundancy in a changing environment, according to an operator's preferences.
The Impact of Interaction with Peers on College Student Development

Since most college student development models take students’ interaction with peers as an important influential factor, by reviewing an amount of related literature, this paper summarized the impact of interaction with peers on college student development in the eight areas categorized by Pascarella and Terenzini. Based on a body of research conducted by analyzing national samples or some small particular samples, using meta-analysis, experiments, or statistical analysis, and controlling potential confounding variables or not, it can be found that interaction with peers have effects in varying degrees on college student development in various aspects.
3D Shape correspondence by isometry-driven greedy optimization

We present an automatic method that establishes 3D correspondence between isometric shapes. Our goal is to find an optimal correspondence between two given (nearly) isometric shapes, that minimizes the amount of deviation from isometry. We cast the problem as a complete surface correspondence problem. Our method first divides the given shapes to be matched into surface patches of equal area and then seeks for a mapping between the patch centers which we refer to as base vertices. Hence the correspondence is established in a fast and robust manner at a relatively coarse level as imposed by the patch radius. We optimize the isometry cost in two steps. In the first step, the base vertices are transformed into spectral domain based on geodesic affinity, where the isometry errors are minimized in polynomial time by complete bipartite graph matching. The resulting correspondence serves as a good initialization for the second step of optimization in which we explicitly minimize the isometry cost via an iterative greedy algorithm in the original 3D Euclidean space. We demonstrate the performance of our method on various isometric (or nearly isometric) pairs of shapes for some of which the ground-truth correspondence is available.
Jellyfish: Networking Data Centers Randomly

Industry experience indicates that the ability to incrementally expand data centers is essential. However, existing high-bandwidth network designs have rigid structure that interferes with incremental expansion. We present Jellyfish, a high-capacity network interconnect which, by adopting a random graph topology, yields itself naturally to incremental expansion. Somewhat surprisingly, Jellyfish is more cost-efficient than a fat-tree, supporting as many as 25% more servers at full capacity using the same equipment at the scale of a few thousand nodes, and this advantage improves with scale. Jellyfish also allows great flexibility in building networks with different degrees of oversubscription. However, Jellyfish's unstructured design brings new challenges in routing, physical layout, and wiring. We describe approaches to resolve these challenges, and our evaluation suggests that Jellyfish could be deployed in today's data centers.
Chapter 13 Virtual Reality

This chapter provides an overview on the use of Virtual Reality (VR) in rehabilitation with respect to recent neuroscience and physical therapy reviews of individuals with motor impairments. A wide range of technologies have been employed to provide rehabilitation supported by VR. Several studies have found evidence of the benefits of VR rehabilitation technologies. However, support for their efficacy is still limited due the lack of generalizable results and the uncoordinated effort of many individual, heterogeneous studies that have been conducted. Although VR has clear potential as a rehabilitation tool to improve treatment outcomes, future trials need to take into account the individual perspective of each patient group and consolidate research methodologies across trials to allow for stronger conclusions across the heterogeneous field of neurorehabilitation. M. Ortiz-Catalan (&) Department of Biomedical Engineering, Chalmers University of Technology, Gothenburg, Sweden e-mail: maxo@chalmers.se S. Nijenhuis Roessingh Research and Development, Enschede, The Netherlands e-mail: s.nijenhuis@rrd.nl K. Ambrosch Department of Biomedical Engineering, University of Applied Sciences Technikum Wien, Vienna, Austria e-mail: kurt.ambrosch@chello.at T. Bovend’Eerdt Department of Human Movement Sciences, Maastricht University, Maastricht, The Netherlands S. Koenig ! B. Lange Institute for Creative Technologies, University of Southern California, Los Angeles, USA e-mail: skoenig@ict.usc. edu B. Lange e-mail: lange@ict.usc. edu J. L. Pons and D. Torricelli (eds.), Emerging Therapies in Neurorehabilitation, Biosystems & Biorobotics 4, DOI: 10.1007/978-3-642-38556-8_13, ! Springer-Verlag Berlin Heidelberg 2014 249 Interventions must be designed with a strong focus on the patient’s needs and clinical outcomes, rather than on the technology available to the clinician.
MicroRNA-Biogenesis and Pre-mRNA Splicing Crosstalk

MicroRNAs (miRNAs) are often hosted in introns of protein-coding genes. Given that the same transcriptional unit can potentially give rise to both miRNA and mRNA transcripts raises the intriguing question of the level of interaction between these processes. Recent studies from transcription, pre-mRNA splicing, and miRNA-processing perspectives have investigated these relationships and yielded interesting, yet somewhat controversial findings. Here we discuss major studies in the field.
Downlink MIMO with Frequency-Domain Packet Scheduling for 3GPP LTE

This paper addresses the problem of frequency domain packet scheduling (FDPS) incorporating spatial division multiplexing (SDM) multiple input multiple output (MIMO) techniques on the 3GPP long term evolution (LTE) downlink. We impose the LTE MIMO constraint of selecting only one MIMO mode (spatial multiplexing or transmit diversity) per user per transmission time interval (TTI). First, we address the optimal MIMO mode selection (multiplexing or diversity) per user in each TTI in order to maximize the proportional fair (PF) criterion extended to frequency and spatial domains. We prove that the SU-MIMO (single-user MIMO) FDPS problem under the LTE requirement is NP-hard and therefore, we develop two approximation algorithms (one with full channel feedback and the other with partial channel feedback) with provable performance bounds. Based on 3GPP LTE system model simulations, the approximation algorithm with partial channel feedback is shown to have comparable performance to the one with full channel feedback, while significantly reducing the channel feedback overhead by nearly 50%.
FAST OBJECTS DETECTION BY VARIANCE-MAXIMIZATION LEARNING OF LIFTING WAVELET FILTERS

A fast objects detecting method is proposed, which is based on the variance-maximization learning of lifting dyadic wavelet filters. First, it is shown that the sum of lifting wavelet coefficients in horizontal and vertical directions defines an elliptic-type of discrete operator containing free parameters. The free parameters are learned so as to maximize the variance of lifting wavelet coefficients for a target object. Since this problem is an ill-posed problem, a regularization method is employed to solve it. Objects in a query image similar to the target object are detected by the use of the learned filter. Simulation concerns the detection of narrow eyes from faces.
A Benchmark Dataset and Saliency-Guided Stacked Autoencoders for Video-Based Salient Object Detection

Image-based salient object detection (SOD) has been extensively studied in past decades. However, video-based SOD is much less explored due to the lack of large-scale video datasets within which salient objects are unambiguously defined and annotated. Toward this end, this paper proposes a video-based SOD dataset that consists of 200 videos. In constructing the dataset, we manually annotate all objects and regions over 7650 uniformly sampled keyframes and collect the eye-tracking data of 23 subjects who free-view all videos. From the user data, we find that salient objects in a video can be defined as objects that consistently pop-out throughout the video, and objects with such attributes can be unambiguously annotated by combining manually annotated object/region masks with eye-tracking data of multiple subjects. To the best of our knowledge, it is currently the largest dataset for video-based salient object detection. Based on this dataset, this paper proposes an unsupervised baseline approach for video-based SOD by using saliency-guided stacked autoencoders. In the proposed approach, multiple spatiotemporal saliency cues are first extracted at the pixel, superpixel, and object levels. With these saliency cues, stacked autoencoders are constructed in an unsupervised manner that automatically infers a saliency score for each pixel by progressively encoding the high-dimensional saliency cues gathered from the pixel and its spatiotemporal neighbors. In experiments, the proposed unsupervised approach is compared with 31 state-of-the-art models on the proposed dataset and outperforms 30 of them, including 19 image-based classic (unsupervised or non-deep learning) models, six image-based deep learning models, and five video-based unsupervised models. Moreover, benchmarking results show that the proposed dataset is very challenging and has the potential to boost the development of video-based SOD.
Preserved Transcallosal Inhibition to Transcranial Magnetic Stimulation in Nondemented Elderly Patients with Leukoaraiosis

Structural corpus callosum (CC) changes in patients with leukoaraiosis (LA) are significantly associated with cognitive and motor impairment. The aim of this study is to investigate the transcallosal fibers functioning by means of transcranial magnetic stimulation (TMS) in elderly patients with LA. The resting motor threshold (rMT), the motor-evoked potentials (MEPs), and the controlateral (cSP) and ipsilateral silent periods (iSP) were determined using single-pulse TMS in 15 patients and 15 age-matched controls. The neuropsychological profile and the vascular burden at brain magnetic resonance imaging (MRI) were concomitantly explored. Patients reported abnormal scores at tests evaluating executive control functions. No significant difference was found in TMS measures of intra- and intercortical excitability. No CC lesion was evident at MRI. Transcallosal inhibitory mechanisms to TMS seem to be spared in LA patients, a finding which is in line with neuroimaging features and suggests a functional integrity of the CC despite the ischemic interruption of corticosubcortical loops implicated in cognition and behavior. The observed neurophysiological finding differs from that reported in degenerative dementia, even in the preclinical or early stage. In our group of patients, the pure extent of LA is more related to impairment of frontal lobe abilities rather than functional callosal changes.
Clinical significance of intracranial developmental venous anomalies

OBJECTIVES Venous angiomas, or developmental venous anomalies (DVAs), represent the most often occurring cerebral vascular malformation. The clinical significance of a DVA is, however, at present unclear. METHODS A retrospective analysis was carried out on two series of consecutive cranial MRIs performed between January 1990 and August 1996 in a university department of neuroradiology and in a large radiological private practice. The medical records of all patients in whom a DVA was diagnosed were screened to identify the specific complaint which necessitated the imaging procedure. RESULTS A total of 67 patients with DVA could be identified. In 12 patients an associated cavernoma was found. The main reason for performing the MRI was the evaluation of seizures or of headaches. In all patients with DVA in whom an intracerebral haemorrhage was diagnosed an associated cavernoma was present at the site of the haemorrhage. None of the 67 patients showed an association between the complaints that led to the MRI and the location of the DVA. CONCLUSIONS DVAs do not seem to be associated with a specific clinical presentation. In a significant percentage of cases, however, coexisting cavernomas are found which have a defined bleeding potential and should be treated independently of the DVA. This study supports the hypothesis that DVAs are a congenital abnormality of venous drainage without clinical significance.