Miracle-dz/UniFAR-FTUs huggingface dataset:
Power Control for Target Tracking in Sensor Networks

We consider the problem of a sensor network tracking a moving target that exhibits a Markov model of mobility. The sensor nodes have adjustable power levels and the precision of the measurement of the target location depends on both the relative distance from the target to the measuring sensor, and on the sensing power level used by that sensor. An important issue in sensor networks is the power effi- ciency, thus we consider the optimization of a family of cost functions that include both the accuracy of a measurement and the power used to do that measurement. We define our problem as a control policy optimization for a partially observed Markov chain. For such scenarios, we derive optimal power control policies based only on partial observations of the target location, and propose hand-off techniques based on this policies.
Mindfulness: Mediating the Relationship between Attachment Security and Parenting Style

The current project examines the relationships between attachment security, parenting style, and mindfulness. The level of mindfulness an individual demonstrates is argued to mediate the relationship between an individual’s attachment security and her respective parenting style. The population is composed of 35 mothers who were drawn from a university clinic setting. Measures utilized included: the Relationship Questionnaire (RQ), which measures adult attachment security, the Mindful Attention Awareness Scale (MAAS), which measures the level of mindfulness an individual demonstrates, and the Parental Authority Questionnaire – Revised (PAQ-R), which measures parenting style attitudes. Results demonstrated no significant relationships between attachment security and parenting style. In examining the relationship between attachment security and mindfulness, individuals designated securely attached demonstrated higher levels of mindfulness than their insecure counterparts. With respect to mindfulness and its relationship with parenting style, mindfulness was positively correlated with an authoritative parenting style but did not demonstrate a significant relationship with either an authoritarian or a permissive parenting style. A test of mediation was conducted to examine the relationship between attachment security and parenting style, with mindfulness as the mediator. The model evidenced a significant reduction in the relationship between attachment security and an authoritative parenting style, suggesting that mindfulness successfully mediated this relationship. Mediation was not found in the case of the permissive and authoritarian styles. Discussions of the methodological approach, the implications of these findings, and future directions for research are presented. v TABLE OF CONTENTS Chapter Page
The influence of mood on the implicit self-appraisal

Вестник ЮУрГУ. Серия «Психология» Introduction. Modern conceptions of the mind make a distinction between deliberate, intentional, or explicit thoughts and feelings, and automatic, unintentional or implicit thoughts and feelings [2]. The theoretical distinction is advanced with a proliferation of measurement methods that assess social constructs (attitudes, stereotypes, and identity) without requiring an act of introspection or self-knowledge. The Implicit Association Test [4, 10] is a popular method, in part, because it is adaptable for many research applications, relatively reliable as a measure of associative strength, elicits strong effects, reveals evaluations that are distinct but related to self-report [11], and shows predictive validity of judgment and behavior across a variety of topics [5]. Method. The Implicit Association Test (IAT) provides a measure of strengths of automatic associations. This measure is computed from performance speeds at two classification tasks in which association strengths influence performance. The apparent usefulness of the IAT may be due to its combination of apparent resistance to self-presentation artifact, its lack of dependence on introspective access to the association strengths being measured [6], and its ease of adaptation to assess a broad variety of socially significant associations. A computer-based measure, the IAT requires that users rapidly categorize two target concepts with an attribute (e.g. the concepts "male" and "female" with the attribute "logical"), such that easier pairings (faster responses) are interpreted as more strongly associated in memory than more difficult pairings (slower responses) [7]. The IAT is thought to measure implicit attitudes: "introspectively unidentified (or inaccurately identified) traces of past experience that mediate favorable or unfavorable feeling, thought, or action toward social objects" [5]. In research, the IAT has been used to develop theories to understand implicit cognition (i.e. cognitive processes of which a person has no conscious awareness). These processes may include memory, perception, attitudes, self-esteem, and stereotypes. Because the IAT requires that users make a series of rapid judgments, researchers believe that IAT scores may also reflect attitudes which people are unwilling to reveal publicly [7]. The IAT may allow researchers to get around the difficult problem of social desirability bias and for that reason it has been used extensively to assess people's attitudes towards commonly stigmatized groups [3]. The IAT’s measure, often referred to as the IAT effect, is based on latencies for two tasks that differ in instructions for using two response keys to classify four categories of stimuli. Table 1 describes the seven steps (blocks) of a typical IAT procedure. THE INFLUENCE OF MOOD ON THE IMPLICIT SELF-APPRAISAL
Boosting rank with predictable training error

Listwise approach is an important method to solve practical Web search problem in learning to rank. In this paper, we first analyze the practical Web search problem and construct the model to solve it. Then we propose an algorithm called DiffRank which can apply boosting technology to learning to rank in listwise. Through theoretical analysis, we prove that the upper bound of training error can be reduced in our proposed algorithm. The experimental results further verify our theoretical analysis and demonstrate that our approach can better perform in practical Web search than other state-of-the-art listwise algorithms.
A Fuzzy Logic-based Personalized Learning System for Supporting Adaptive English Learning

As a nearly global language, English as a Foreign Language (EFL) programs are essential for people wishing to learn English. Researchers have noted that extensive reading is an effective way to improve a person's command of English. Choosing suitable articles in accordance with a learner's needs, interests and ability using an elearning system requires precise learner profiles. This paper proposes a personalized English article recommending system, which uses accumulated learner profiles to choose appropriate English articles for a learner. It employs fuzzy inference mechanisms, memory cycle updates, learner preferences and analytic hierarchy process (AHP) to help learners improve their English ability in an extensive reading environment. By using fuzzy inferences and personal memory cycle updates, it is possible to find an article best suited for both a learner’s ability and her/his need to review vocabulary. After reading an article, a test is immediately provided to enhance a learner’s memory for the words newly learned in the article. The responses of tests can be used to explicitly update memory cycles of the newly-learned vocabulary. In addition, this paper proposes a methodology that also implicitly modifies memory cycles of words that were learned before. By intensively reading articles recommended through the proposed approach, learners comprehend new words quickly and review words that they knew implicitly as well, thereby efficiently improving their vocabulary volume. Analyses of learner achievements and questionnaires have confirmed that the adaptive learning method presented in this study not only enhances the English ability of learners but also helps maintaining their learning interest.
What Is It That Color Determinants Determine? The Relation Between the Rorschach Inkblot Method and Cognitive Object-Recognition Processes

We sought to demonstrate a relation between the Determinants in the Rorschach Inkblot Method (Rorschach, 1921) and fundamental properties of the participant's cognitive (visual) system by examining whether the report about Color Determinants is related to basic cognitive processes concerned with color of visual objects. In Experiment 1, we established an object-naming task that is sensitive to the objects' color. Participants were strongly influenced by the object's color, responding fastest when objects appeared in their typical color and slowest when the object's color was atypical. In Experiment 2, we examined the relationship between the Color Determinants in the Rorschach Inkblot Method and the magnitude of the color effect in the object-naming task of Experiment 1. It was found that the effect of color in the object-naming task was correlated with the type of color responses in the Rorschach Inkblot Method. The results support an “early” cognitive account of the Determinants. We discuss implications concerning the theory of the Rorschach and the relation between emotion, personality, and cognition.
Sibling Relationships, Stress and Well-being during Early Adolescence

Sibling Relationships, Stress and Well-being during Early Adolescence Alisa Cox Van Langeveld School of Family Life Doctor of Philosophy This study examined whether the quality of the sibling relationship can alter the negative impact of stress on child well being. Participants were of 311 families (236 two parent families and 75 single parent families) with an adolescent child (M age of child at Time 1 = 11.25, SD = .99, 51% female) who took part in the Flourishing Families Project. Data were assessed using both a multiple time point cross-section and a two wave longitudinal design. Hierarchal linear regression suggested that when assessing the direct effects of sibling on well being, sibling affection is a better predictor of well being, but when assessing indirect effects, sibling conflict is a better predictor. Little evidence was found to support the idea that siblings moderate the impact of stress by buffering or decreasing the negative impact of stress. Results did indicate that sibling conflict was a salient moderator of stress in that conflict exacerbates the already negative impact of stress. Results from this study also suggest that when assessing the buffering or exacerbating impact of siblings, cross-sectional data produces better explanatory power than when these constructs are assessed longitudinally. However a single time point, cross-sectional design does not account for dynamic changes over time in either the sibling relationship, the level of stress or well being. Research designs such as multiple time, point cross-sectional studies or growth curve analyses are recommended.
Role of cAMP Cascade in Synaptic Stability and Plasticity: Ultrastructural and Physiological Analyses of Individual Synaptic Boutons in Drosophila Memory Mutants

Mutations of the genes rutabaga (rut) and dunce (dnc) affect the synthesis and degradation of cAMP, respectively, and disrupt learning inDrosophila. Combined ultrastructural analysis and focal electrophysiological recording in the larval neuromuscular junction revealed a loss of stability and fine tuning of synaptic structure and function in both mutants. Increased ratios of docked/undocked vesicles and poorly defined synaptic specializations characterizeddnc synapses. In contrast, rut boutons possessed fewer, although larger, synapses with lower proportions of docked vesicles. At reduced Ca2+ levels, decreased quantal content coupled with an increase in failure rate was seen inrut boutons and reduced pair-pulse facilitation were found in both rut and dnc mutants. At physiological Ca2+ levels, strong enhancement, instead of depression, in evoked release was observed in somednc and rut boutons during 10 Hz tetanus. Furthermore, increased variability of synaptic transmission, including fluctuation and asynchronicity of evoked release, paralleled an increase in synapse size variation in both dnc andrut boutons, which might impose problems for effective signal processing in the nervous system. Pharmacological and genetic studies indicated broader ranges of physiological alteration bydnc and rut mutations than either the acute effects of cAMP analogs or the available mutations that affect cAMP-dependent protein kinase (PKA) activity. This is consistent with previous reports of more severe learning defects in dncand rut mutations than these PKA mutants and allows identification of the phenotypes involving long-term developmental regulation and those conferred by PKA.
Novelty-based Incremental Document Clustering for On-line Documents

Document clustering has been used as a core technique in managing vast amount of data and providing needed information. In on-line environments, generally new information gains more interests than old one. Traditional clustering focuses on grouping similar documents into clusters by treating each document with equal weight. We proposed a novelty-based incremental clustering method for on-line documents that has biases on recent documents. In the clustering method, the notion of ‘novelty’ is incorporated into a similarity function and a clustering method, a variant of the K-means method, is proposed. We examine the efficiency and behaviors of the method by experiments.
Template Based Inference in Symmetric Relational Markov Random Fields

Relational Markov Random Fields are a general and flexible framework for reasoning about the joint distribution over attributes of a large number of interacting entities. The main computational difficulty in learning such models is inference. Even when dealing with complete data, where one can summarize a large domain by sufficient statistics, learning requires one to compute the expectation of the sufficient statistics given different parameter choices. The typical solution to this problem is to resort to approximate inference procedures, such as loopy belief propagation. Although these procedures are quite efficient, they still require computation that is on the order of the number of interactions (or features) in the model. When learning a large relational model over a complex domain, even such approximations require unrealistic running time. In this paper we show that for a particular class of relational MRFs, which have inherent symmetry, we can perform the inference needed for learning procedures using a template-level belief propagation. This procedure's running time is proportional to the size of the relational model rather than the size of the domain. Moreover, we show that this computational procedure is equivalent to sychronous loopy belief propagation. This enables a dramatic speedup in inference and learning time. We use this procedure to learn relational MRFs for capturing the joint distribution of large protein-protein interaction networks.
Predominance of Abdominal Visceral Adipose Tissue Reflects the Presence of Aortic Valve Calcification

Background. Aortic valve calcification (AVC) is a common feature of aging and is related to coronary artery disease. Although abdominal visceral adipose tissue (VAT) plays fundamental roles in coronary artery disease, the relationship between abdominal VAT and AVC is not fully understood. Methods. We investigated 259 patients who underwent cardiac and abdominal computed tomography (CT). AVC was defined as calcified lesion on the aortic valve by CT. %abdominal VAT was calculated as abdominal VAT area/total adipose tissue area. Results. AVC was detected in 75 patients, and these patients showed higher %abdominal VAT (44% versus 38%, p < 0.05) compared to those without AVC. When the cutoff value of %abdominal VAT was set at 40.9%, the area under the curve to diagnose AVC was 0.626. Multivariable logistic regression analysis showed that age (OR 1.120, 95% CI 1.078–1.168, p < 0.01), diabetes (OR 2.587, 95% CI 1.323–5.130, p < 0.01), and %abdominal VAT (OR 1.032, 95% CI 1.003–1.065, p < 0.05) were independent risk factors for AVC. The net reclassification improvement value for detecting AVC was increased when %abdominal VAT was added to the model: 0.5093 (95% CI 0.2489–0.7697, p < 0.01). Conclusion. We determined that predominance of VAT is associated with AVC.
Mycobacterium arupense in Cancer Patients

AbstractMycobacterium arupense is a slow-growing, nonchromogenic, acid-fast bacillus. Its clinical spectrum, epidemiology, and frequency of colonization versus true infection remain unknown. We evaluated the clinical significance of M arupense and positive cultures from cancer patients.We retrospectively reviewed records of all cancer patients treated at our institution between 2007 and 2014 to identify those who had positive cultures for M arupense. Mycobacterium arupense was identified by sequencing the 16S rRNA and hsp65 genes. A total of 53patients had positive cultures, 100% of which were isolated from respiratory specimens. Of these, 7 patients met the American Thoracic Society/Infectious Diseases Society of America criteria for a definitive diagnosis of M arupense infection, 14 cases were considered to be probable infections, and 29 cases were considered to be possible infections. Of the included patients, 13 received therapy for M arupense infection and 40 did not.The outcomes of treated and untreated patients did not differ significantly. No relapses of M arupense infection. In addition, there were no M arupense-related deaths in either group.In cancer patients, M arupense appears to be mostly a commensal organism rather than a pathogen. Patients who did or did not receive treatment had similar outcomes. Validation of these findings in a larger prospective trial is warranted.
Truthful mechanisms with implicit payment computation

It is widely believed that computing payments needed to induce truthful bidding is somehow harder than simply computing the allocation. We show that the opposite is true for single-parameter domains: creating a randomized truthful mechanism is essentially as easy as a single call to a monotone allocation function. Our main result is a general procedure to take a monotone allocation rule and transform it (via a black-box reduction) into a randomized mechanism that is truthful in expectation and individually rational for every realization. Moreover, the mechanism implements the same outcome as the original allocation rule with probability arbitrarily close to 1, and requires evaluating that allocation rule only once. Because our reduction is simple, versatile, and general, it has many applications to mechanism design problems in which re-evaluating the allocation function is either burdensome or informationally impossible. Applying our result to the multi-armed bandit problem, we obtain truthful randomized mechanisms whose regret matches the information-theoretic lower bound up to logarithmic factors, even though prior work showed this is impossible for truthful deterministic mechanisms. We also present applications to offline mechanism design, showing that randomization can circumvent a communication complexity lower bound for deterministic payments computation, and that it can also be used to create truthful shortest path auctions that approximate the welfare of the VCG allocation arbitrarily well, while having the same running time complexity as Dijkstra's algorithm.
Cerebral White Matter Lesions and Lacunar Infarcts Contribute to the Presence of Mild Parkinsonian Signs

Background and Purpose— Mild parkinsonian signs (MPS) are common in elderly people and may be an early stage of parkinson(ism). They might be related to cerebral small-vessel disease, although this association remains incompletely understood. To identify subjects at early stages of the disease, we investigated whether the presence of MPS was dependent on the severity and location of small-vessel disease, including white matter lesions and lacunar infarcts. Methods— Four hundred thirty individuals, with small-vessel disease, aged between 50 and 85 years, without dementia or parkinsonism, were included in this analysis and underwent MRI scanning. The number and location of lacunar infarcts were rated. White matter lesion volume was assessed by manual segmentation with automated delineating of different regions. Presence of MPS was based on the motor section of the Unified Parkinson's Disease Rating Scale. Associations were determined using logistic regression analysis adjusted for age, sex, and total brain volume. Results— Severe white matter lesions and the presence of lacunar infarcts were independently associated with the presence of MPS (OR, 2.6; 95% CI, 1.3–4.9 and OR, 1.8; 95% CI, 1.0–3.0). Frontal and parietal white matter lesions and, to a lesser extent, lacunar infarcts in the thalamus were associated with a higher risk of MPS. The presence of lacunar infarcts was independently related to the bradykinesia category of parkinsonian signs. Conclusions— This study shows that severe small-vessel disease, especially at certain locations, is associated with MPS signs in older adults. Our findings suggest that small-vessel disease interrupts basal ganglia–thalamocortical circuits involving both the frontal and parietal lobes and hence may result in MPS.
Distributed Elastic Net Regularized Blind Compressive Sensing for Recommender System Design

Design of recommender system following the latent factor model is widely cast as a matrix factorization problem yielding a rating matrix, which is a product of a dense user and a dense item factor matrices. A dense user factor matrix is a credible assumption as all users are expected to have some degree of affinity towards all the latent factors. However, for items it's not a reasonable supposition as no item is expected to possess all the traits (factors). In this work, we propose a matrix factorization model which yields a dense user but a sparse item factor matrix; having equivalence to Blind Compressive Sensing (BCS) formulation. Basic BCS framework is augmented with an added elastic net regularization term. The addition helps in capturing correlation between different item latent factors. Despite the efficiency of matrix factorization approach, it's not feasible to apply the techniques for very large datasets (rating matrices). For this purpose, we employ Divide and Combine (DnC) approach -- wherein proposed method is applied to distinct subsets of the rating matrix simultaneously and resulting estimates combined to yield the final result. The (randomized) DnC approach retains the convergence guarantees of matrix factorization. Experiments were conducted on real world Movielens dataset and our technique was compared against popular matrix factorization methods. The results indicate the superiority of our method in terms of both accuracy and speed.
SURVEY OF DATA MANAGEMENT SYSTEMS USED IN THE PULP AND PAPER INDUSTRY

Pulp and paper mills are increasingly implementing process information systems with the goal of better tracking, troubleshooting and optimizing their processes. In the field of process engineering as well as in the field of business process engineering, these information systems are playing an increasingly important role. However, their potential is far from being fully exploited, in large part due to a lack of dedicated resources for applications development at mills. There is also a rapid growth in applications capability being driven by system vendors, expanding the potential for finding value in process data. Some applications in the pulp and paper industry discussed in this paper include: 1. data historians for storing process data and using this data for process trouble-shooting; 2. enterprise asset management systems for streamlining maintenance tasks; 3. manufacturing execution systems for streamlining manufacturing, distribution operations and business processes.