Miracle-dz/UniFAR-FTUs huggingface dataset:
Daughters Caring for Dying Parents: A Process of Relinquishing

Caring for elderly, dying parents is challenging for daughters as they try to balance other obligations and responsibilities. The purpose of this grounded theory study was to explain the domain of daughters' caregiving experiences in Newfoundland and Labrador, Canada. The primary author interviewed 12 women whose parents had died. Three types of turmoil (emotional, relational, and societal) emerged as the central issue for these women. The authors discovered a substantive theory of Relinquishing with interdependent processes of Keeping Vigil, Navigating Systems, Facing Loss, and an end process of Coming to Terms. In moving through the process of Relinquishing, social conditions of personal ideals, family expectations, and societal demands determine strategies employed by any one daughter to manage her turmoil. The findings fill a gap in knowledge related to daughters' caregiving for dying parents by contributing a theoretical framework that will inform women, health care providers, researchers, and health policy makers.
A fast sub-optimal antenna selection algorithm in mimo systems

MIMO systems can offer high multiplexing and diversity gain, but the increased complexity hampers the practical applications. Through antenna selection, the complexity and cost can be greatly reduced while the gain of MIMO systems can be reserved. In this paper, a fast sub-optimal antenna selection algorithm based on norm and correlation is proposed for maximizing the channel capacity. Compared with the near-optimal selection algorithm of M. Gharavi-Alkhansari and A. Greshman (2004), our sub-optimal algorithm can capture most of the capacity property while the complexity and memory requirement are greatly decreased. Simulation results show that our algorithm is very effective when SNR is high and the number of the selected antennas is relatively small. Especially for a rank deficient channel matrix, the sub-optimal algorithm outperforms the norm-based selection algorithm. Our algorithm also shows capacity advantages when compared with the correlation based method (CBM) algorithm of Y.S. Choi et al. (2003)
Optimal Trading in a Two-Sided Limit Order Book

This paper studies four trading algorithms of a professional trader, in a realistic two-sided limit order book whose dynamics are driven by the order book events. The identity of the trader can be either privileged or regular, either a hedge fund or a brokery agency. The speed and cost of trading can be balanced by properly choosing active strategies on the displayed orders in the book and passive strategies on the hidden orders within the spread. We shall show that the price switching algorithms provide lower and upper bounds of the singular trading algorithms. For both a privileged trader and a regular trader, the optimal price switching strategy exists and is expressed in terms of the value function. A parallelizable algorithm to numerically compute the value function and optimal price switching strategy for the discretized state process is provided.
Sensor redundancy check without geometric information

In this paper we study sufficient conditions for sensor redundancy that can be used without any coordinate or geometric information. We assume boolean sensing model: each sensor observes a disc of radius <i>r</i><sub><i>s</i></sub> around his position, and can communicate with other sensors up to a distance <i>r</i><sub><i>c</i></sub>. We prove that if <i>r</i><sub><i>s</i></sub> <i>≥ r</i><sub><i>c</i></sub>, any sensor with five neighbors that are pairwise <i>not</i> neighbors is redundant, and this is best possible. With a slightly stronger model that sensors can distinguish close and distant neighbors, we generalizes to the case where <i>r</i><sub><i>s</i></sub> <i>≤ r</i><sub><i>c</i></sub> in several ways: any sensor is redundant if it has <i>m</i> neighbors within distance <i>α</i><sub><i>m</i></sub><i>r</i><sub><i>s</i></sub>, being pairwise farther than <i>r</i><sub><i>s</i></sub>, where <i>m</i> = 3, 4, 5 and α<sub>3</sub> ≈ 0.62, α<sub>4</sub> ≈ 0.81, α<sub>5</sub> = 1; or if it has <i>g</i>(α) neighbors within distance <i>r</i><sub><i>s</i></sub> that are pairwise farther than α<i>r</i><sub><i>s</i></sub>, where α ∈]0, 1], <i>g</i>(1) = 5 and 15 ≤ <i>g</i>((√6 -- √2)/2) ≤ 16.
An efficient algorithm to compute differences between structured documents

SGML/XML are having a profound impact on data modeling and processing. We present an efficient algorithm to compute differences between old and new versions of an SGML/XML document. The difference between the two versions can be considered to be an edit script that transforms one document tree into another. The proposed algorithm is based on a hybridization of bottom-up and top-down methods: The matching relationships between nodes in the two versions are produced in a bottom-up manner and then the top-down breadth-first search computes an edit script. Faster matching is achieved because the algorithm does not need to investigate the possible existence of matchings for all nodes. Furthermore, it can detect structurally meaningful changes such as the movement and copy of a subtree as well as simple changes to the node itself like insertion, deletion, and update.
Collective belief revision in linear algebra

Although the logic of belief update has mainly concerned a belief state of one agent thus far, the real world settings require us to implement simultaneous belief changes. Here, however, we need to manage so many indices: agent names, time stamps, and the difference of information. In this paper, we introduce the notation of vectors and matrices for the simultaneous informing action. By this, we show that a matrix can represent a public announcement and/or a consecutive message passing, with the time of the change of belief states properly. A collective belief state multiplied by a communication matrix, including matrices of accessibility in Kripke semantics, becomes a hypercuboid.
Performance Analysis of Mean Value-Based Power Allocation with Primary User Interference in Spectrum Sharing Systems

In this paper, we provide an exact expression for the ergodic capacity of the secondary user, and a unified closed-form expression for its bounds with taking the consideration of t he primary interference at the secondary receiver. In addit ion, a simple but accurate approximation of the the ergodic capacity of the primary user is presented. Moreover, a primary user capacity loss based power allocation scheme for the secondary user is also proposed. Finally, we compare the performance of the two power allocation schemes in terms of the sum capacity. The results are validated through using numerical results and simulations.
The Somnolent Youth-Sleep and the Influence of Exercise: A Narrative Review

Sleep is crucial to the physiological and psychological development of youth. The implications of insufficient sleep on learning and school performance are well documented. However, adequate sleep often goes unmet due to a combination of biological, societal, and environmental factors, especially during adolescence. In the present review, the changes to adolescent sleep patterns, and its consequences on cognition and learning are briefly explored. We then review the empirical literature of the role of exercise in regulating adolescent sleep patterns, and its potential mechanisms.
An exploratory study of feature location process: Distinct phases, recurring patterns, and elementary actions

Developers often have to locate the parts of the source code that contribute to a specific feature during software maintenance tasks. This activity, referred to as feature location in software engineering, is a human- and knowledge-intensive process. Researchers have investigated information retrieval, static/dynamic analysis based techniques to assist developers in such feature location activities. However, little work has been done on better understanding how developers perform feature location tasks. In this paper, we report an exploratory study of feature location process, consisting of two experiments in which developers were given unfamiliar systems and asked to complete six feature location tasks in two hours. Our study suggests that feature location process can be understood hierarchically at three levels of granularities: phase, pattern, and action. Furthermore, our study suggests that these feature-location phases, patterns and actions can be effectively imparted to junior developers and consequently improve their performance on feature location tasks. Our results open up new opportunities to feature location research, which could lead to better tool support and more rigorous feature location process.
Automatic Parallel Program Generation and Optimization from Data Decompositions

Data decomposition is probably the most successful method for generating parallel programs. In this paper a general framework is described for the automatic generation of parallel programs based on a separately specified decomposition of the data. To this purpose, programs and data decompositions are expressed in a calculus, called Vcal. It is shown that by rewriting calculus expressions, Single Program Multiple Data (SPMD) code can be generated for shared-memory as well as distributed-memory parallel processors. Optimizations are derived for certain classes of access functions to data structures, subject to block, scatter, and block/scatter decompositions. The presented calculus and transformations are language independent.
Online Clustering of Linguistic Data

Clustering text data online as it comes in is a difficult problem. It is both hard to capture a meaningful notion of linguistic similarity and to cluster large amounts of data in a single pass. This problem is especially challenging because most known algorithms that ensure tight clusterings are inefficient on large datasets. While significant work has been done on text clustering, it has not been fully explored. In this paper, we discuss previous methods in text clustering and then develop a single-pass text clustering algorithm designed specifically for clustering news stories, (but more widely applicable) and examine its empirical behavior. We then analyze some of its key design features and compare them to possible alternative methods. Finally, we discuss possibilities for further improvement of our algorithm.
Reciprocal inhibition between Pax7 and muscle regulatory factors modulates myogenic cell fate determination

Postnatal growth and regeneration of skeletal muscle requires a population of resident myogenic precursors named satellite cells. The transcription factor Pax7 is critical for satellite cell biogenesis and survival and has been also implicated in satellite cell self-renewal; however, the underlying molecular mechanisms remain unclear. Previously, we showed that Pax7 overexpression in adult primary myoblasts down-regulates MyoD and prevents myogenin induction, inhibiting myogenesis. We show that Pax7 prevents muscle differentiation independently of its transcriptional activity, affecting MyoD function. Conversely, myogenin directly affects Pax7 expression and may be critical for Pax7 down-regulation in differentiating cells. Our results provide evidence for a cross-inhibitory interaction between Pax7 and members of the muscle regulatory factor family. This could represent an additional mechanism for the control of satellite cell fate decisions resulting in proliferation, differentiation, and self-renewal, necessary for skeletal muscle maintenance and repair.
Constraint-based self-adaptation of wireless sensor networks

In recent years, the Wireless Sensor Networks (WSNs) have become a useful mechanism to monitor physical phenomena in environments. The sensors that make part of these long-lived networks have to be reconfigured according to context changes in order to preserve the operation of the network. Such reconfigurations require to consider the distributed nature of the sensor nodes as well as their resource scarceness. Therefore, self-adaptations for WSNs have special requirements comparing with traditional information systems. In particular, the reconfiguration of the WSN requires a trade-off between critical dimensions for this kind of networks and devices, such as resource consumption or reconfiguration cost. Thus, in this paper, we propose to exploit Constraint-Satisfaction Problem (CSP) techniques in order to find a suitable configuration for self-adapting WSNs, modelled using a Dynamic Software Product Line (DSPL), when the context changes. We exploit CSP modeling to find a compromise between contradictory dimensions. To illustrate our approach, we use an Intelligent Transportation System scenario. This case study enables us to show the advantages of obtaining suitable and optimized configurations for self-adapting WSNs.
Face recognition: A holistic approach review

Face recognition has become more significant and relevant in recent years owing to its potential applications. Face recognition has far reaching benefits to corporations, the government and the greater society. Face recognition is basically identifying individuals by their faces. There are many face recognition approaches which are generally classified as feature based and holistic approaches. Presently there are a very small number of studies which compare both these approaches. There is a tremendous increase in face recognition research nowadays; primarily because of the various negative events taking place around the globe. With the increase in the number of proposed algorithms and techniques the survey and evaluation of these algorithms and techniques becomes more vital to provide a boost to the research activities. The primary aim of this paper is to provide a critical summary of the existing literature on human face recognition over the past decade with special reference to holistic approaches to face detection.
Sisterhood of Classifiers : A Comparative Study of Naive Bayes and Noisy-or Networks

Classification is a task central to many machine learning problems. In this paper we examine two Bayesian network classifiers, the naive Bayes and the noisy-or models. They are of particular interest because of their simple structures. We compare them on two dimensions: expressive power and ability to learn. As it turns out, naive Bayes, noisy-or, and logistic regression classifiers all have equivalent expressiveness. We show mathematical derivations of how to transform a classifer in one model into the other two. These classifiers differ on their ability to learn though. We conducted an experiment confirming the intuition that naive Bayes performs better than noisy-or when the data fits its independence assumptions, and vice versa. However, we still do not have a clear set of criteria for determining under exactly what conditions would each classifier excel. Further study of the strenghts and weaknesses of each classifier should provide deeper insight on how to improve the current models. One possible extension would be to combine the naive Bayes and noisy-or model so that the network will more closely depict the actual relationship between the attributes.
Developmental change in numerical estimation.

Mental representations of numerical magnitude are commonly thought to undergo discontinuous change over development in the form of a "representational shift. " This idea stems from an apparent categorical shift from logarithmic to linear patterns of numerical estimation on tasks that involve translating between numerical magnitudes and spatial positions (such as number-line estimation). However, the observed patterns of performance are broadly consistent with a fundamentally different view, based on psychophysical modeling of proportion estimation, that explains the data without appealing to discontinuous change in mental representations of numerical magnitude. The present study assessed these 2 theories' abilities to account for the development of numerical estimation in 5- through 10-year-olds. The proportional account explained estimation patterns better than the logarithmic-to-linear-shift account for all age groups, at both group and individual levels. These findings contribute to our understanding of the nature and development of the mental representation of number and have more general implications for theories of cognitive developmental change.
The CB1 receptor antagonist rimonabant reverses the diet‐induced obesity phenotype through the regulation of lipolysis and energy balance

We investigated the molecular events involved in the long‐lasting reduction of adipose mass by the selective CB1 antagonist, SR141716. Its effects were assessed at the transcriptional level both in white (WAT) and brown (BAT) adipose tissues in a diet‐induced obesity model in mice. Our data clearly indicated that SR141716 reversed the phenotype of obese adipocytes at both macroscopic and genomic levels. First, oral treatment with SR141716 at 10 mg/kg/d for 40 days induced a robust reduction of obesity, as shown by the 50% decrease in adipose mass together with a major restoration of white adipocyte morphology similar to lean animals. Second, we found that the major alterations in gene expression levels induced by obesity in WAT and BAT were mostly reversed in SR141716‐treated obese mice. Importantly, the transcriptional patterns of treated obese mice were similar to those obtained in the CB1 receptor knockout mice fed a high‐fat regimen and which are resistant to obesity, supporting a CB1 receptor‐mediated process. Functional analysis of these modulations indicated that the reduction of adipose mass by the molecule resulted from an enhanced lipolysis through the induction of enzymes of the β‐oxidation and TCA cycle, increased energy expenditure, mainly through futile cycling (calcium and substrate), and a tight regulation of glucose homeostasis. These changes accompanied a significant cellular remodeling and contributed to a reduction of the obesity‐related inflammatory status. In addition to a transient reduction of food consumption, increases of both fatty acid oxidation and energy expenditure induced by the molecule summate leading to a sustained weight loss. Altogether, these data strongly indicate that the endocannabinoid system has a major role in the regulation of energy metabolism.
Image based classification of normal and special people using constrained local model

Detection and diagnosis of mental retardation in early stage is a problem for almost all Pediatric Doctors and Parents. The image database has been constructed by capturing face images of local normal and special persons and this work will help in early detection and diagnosis of Cognitive diseases. This paper focuses on implementation of Constrained Local Model (CLM) to classify the normal and special persons. CLM model uses two feature model, shape model and patch model to extract the desire features of facial images. CLM is based on shape model and patch model. The shape model is constructed using Principle Component Analysis (PCA). Patch model is constructed using Support vector machine (SVM). This paper is based on shape model. Features extracted from this approaches are used as input o supervised classifier such as Linear Discriminate Classifier (LDC) and Quadrature discriminate Classifier (QDC).
Increasing Spot Instances Reliability Using Dynamic Scalability

Traditionally, Infrastructure as a Service (IaaS)providers deliver their services as Reserved or On-Demand instances. Spot Instances (SIs) is a complementary service that allows customers to bid on the free capacity at the provider data centers. Therefore, the decrease in the free capacity may result in terminating instances abruptly. To ensure fair trading, the provider does not charge customers for the interrupted partial hours. However, SIs price history traces analysis shows that uncharged time could rise up to 30% of the instance total run time, which means a reduction in the provider's profit. In this paper, we propose Elastic Spot Instances (ESIs) approach. It is a trade-off between the price and the total run time, where instead of abruptly terminating the SIs, the provider scales down their capacity proportionally to the increase in the price. Our approach delegates the task of interrupting the instances into the customers, but at the same time keeps the control on the provider side to isolate SIs' impact on the other services at overloaded time. Our approach doesn't imply an additional overhead or complex modification to current IaaS, while it consumes interfaces that are available by most of nowadays virtualization technologies.
Stanniocalcin 1 and ovarian tumorigenesis.

BACKGROUND Stanniocalcin 1 (STC1) is a secreted glycoprotein hormone. High expression of STC1 has been associated with several cancers including ovarian cancer, but its role in the development of ovarian cancer is not clear. METHODS We used five human ovarian epithelial cancer cell lines (OVCA420, OVCA432, OVCA433, SKOV3, and HEY), immortalized human ovarian surface epithelial cells (T29 and T80), ovarian cancer tissues from 342 patients, serum from 73 ovarian cancer patients and from58 control subjects, and 116 mice, with six or eight per group. Protein expression was assessed. Cells overexpressing STC1 protein were generated by ectopic expression of human STC1 cDNA. STC1 expression was silenced by using small interfering RNA against STC1. Cell proliferation, migration, colony formation, and apoptosis were assessed. Xenograft tumor growth in mice was studied. Neutralizing anti-STC1 antibody was used to inhibit STC1 function. All statistical tests were two-sided. RESULTS STC1 protein expression was higher in all human ovarian cancer cell lines examined than in immortalized human ovarian epithelial cell lines, higher in ovarian cancer tissue than in normal ovarian tissue (P < .001), and higher in serum from ovarian cancer patients than from control subjects (P = .021). Ovarian cancer cells with STC1 overexpression, compared with corresponding control cells, had increased cell proliferation, migration, and colony formation in cell culture and increased growth of xenograft tumors in mice. These activities in normal or malignant ovarian cells with STC1 overexpression, compared with control cells, were also accompanied by increased expression of cell cycle regulatory proteins and antiapoptotic proteins but decreased cleavage of several caspases. Within 24 hours of treatment, apoptosis in cultures of HEY ovarian cancer cells treated with neutralizing anti-STC1 monoclonal antibody was higher (17.3% apoptotic cells) than that in cultures treated with mouse IgG control cells (4.4%) (12.9% difference, 95% confidence interval = 11.6% to 14.2%). CONCLUSIONS STC1 protein may be involved in ovarian tumorigenesis.