Miracle-dz/UniFAR-FTUs huggingface dataset:
An I/Q active balanced harmonic mixer with IM2 cancelers and a 45/spl deg/ phase shifter

An active I/Q harmonic mixer with IM2 cancelers and a 45/spl deg/ phase shifter for direct downconversion has two advantages. One is small self-mixing dc offset because of harmonic mixing, and the other is small and frequency insensitive 2/sup nd/ order intermodulation (IM2). The proposed 45/spl deg/ phase shifter has low element sensitivity. The measured 37 dBm IIPB and 3/spl deg/ phase error indicate that the integrated direct downconverter with harmonic mixers is applicable even for QPSK signals that have an amplitude modulation component.
Estimation of the Uncertainty in Time Domain Indices of RR Time Series

A method for estimating the uncertainty in time-domain indices of RR time series is described. The method relies on the central limit theorem that states that the distribution of a sample average of independent samples has an uncertainty that asymptotically approaches to the sample standard deviation divided by the square root of the number of samples. Because RR time series cannot be characterized by a set of independent samples, we propose to estimate the uncertainty of indices by computing them in blocks that satisfy that the obtained partial indices are independent. We propose a methodology to search sets of independent partial indices and apply this methodology to the estimation of the uncertainty in the mean RR, SDRR, and r-msDD indices. The results show that the uncertainty can be higher than the 10% of the index for the SDRR and even higher for the r-msDD. Moreover, a statistical test for the difference of two indices is proposed
Self-efficacy and quality of life after low-intensity neuropsychological rehabilitation: A pre-post intervention study.

BACKGROUND Being highly self-efficacious is a key factor in successful chronic disease self-management. It is unknown whether neuropsychological rehabilitation improves self-efficacy in managing the consequences of brain injury. OBJECTIVE To investigate whether levels of general and brain injury specific self-efficacy and quality of life (QoL) increased after neuropsychological rehabilitation and whether cognitive performance was associated with self-efficacy. METHODS We conducted a retrospective clinical cohort study of 62 patients with acquired brain injury and cognitive complaints with measurements before start and after completion of treatment. QoL was measured with the visual analogue scale (EQ VAS) of the EuroQol (EQ-5D); self-efficacy with the TBI Self-efficacy Questionnaire (SEsx) and the General Self-efficacy Scale (GSES). Cognitive performance was measured as a compound score of tests for memory, attention and information processing speed. RESULTS Self-efficacy for managing brain injury-specific symptoms and QoL increased significantly after neuropsychological rehabilitation. Both general and brain injury-specific self-efficacy were positively associated with QoL after completion of the programme. Cognitive performance was not associated with self-efficacy for managing brain injury-specific symptoms nor with general self-efficacy. CONCLUSIONS Self-efficacy and QoL improve after treatment. Further research is needed to identify the specific ingredients responsible for improvement of self-efficacy in patients with cognitive complaints.
An Efficient and Fast Parzen-Window Density Based Clustering Method for Large Data Sets

Density based clustering technique like DBSCAN finds arbitrary shaped clusters along with noisy outliers. DBSCAN finds the density at a point by counting the number of points falling in a sphere of radius epsi and it has a time complexity of O(n2). Hence it is not suitable for large data sets. The proposed method in this paper is an efficient and fast Parzen-Window density based clustering method which uses (i) prototypes to reduce the computational burden, (ii) a smooth kernel function to estimate density at a point and hence the estimated density is also varies smoothly. Enriched prototypes are derived using counted leaders method. These are used with a special form of the Gaussian kernel function which is radially symmetrical and hence the function can be completely specified by a variance parameter only. The proposed method is experimentally compared with DBSCAN which shows that it is a suitable method for large data sets.
Strategic Data Planning: Lessons From the Field

In spite of strong conceptual arguments for the value of strategic data planning as a means to increase data integration into large organizations, empirical research has found more evidence of problems than of success. In this paper, four detailed case studies of SDP efforts, along with summaries of five previously reported efforts, are analyzed. Fifteen specific propositions are offered, with two overall conclusions. The first conclusion is that SDP, though conceived of as a generally appropriate method, may not be the best planning approach in all situations. The second conclusion is that the SDP method of analyzing business functions and their data requirements may not be the best way to develop a "data architecture," given the required level of commitment of talented individuals, the cost, the potential errors, and the high level of abstraction of the result. These lessons can aid practitioners in deciding when to use SDP and guide them as they begin the process of rethinking and modifying the SDP to be more effective.
Impact of attribution retraining with students enrolled in an Internet-based instructional technology course at a community college

Impact of Attribution Retraining with Students Enrolled in an Internet-based Instructional Technology Course at a Community College Bonnie McCall Ordonez This study explores the use of attribution retraining in a community college, distance learning course. Attribution training is a form of psychotherapy used in an attempt to shift an individual’s attitude, motivation, and locus of control (LOC). Locus of control is a measure of how one attributes success and failure. Those with internal locus of control believe they are responsible and control their fate while those with external locus of control tend to look for fault outside of themselves. The purpose of the study was to examine the impact of attribution training, which consisted of a multimedia, interactive intervention called Just Think It, on an online instructional technology course. The research questions examined the impact of attribution retraining on locus of control scores, grade point average, and course retention. Little research has been done on the use of attribution retraining to alter locus of control in a distance learning environment. The participants consisted of students enrolled in the online course, EDU202 Instructional Technology in the summer and fall 2007 semesters. A quasiexperimental research design was used whereas treatment and control groups were each given a pre and posttest using Rotter’s LOC survey. The treatment group received the intervention and the control group did not. Results were compared along with final grade averages and course retention statistics. Results indicated that no significant change existed as the result of the intervention for locus of control or grade point average. However, course retention rates proved to be slightly higher for the treatment group than those of the control group.
Incremental Surface Extraction from Sparse Structure-from-Motion Point Clouds

In this paper we propose a new method to incrementally extract a surface from a consecutively growing Structure-from-Motion (SfM) point cloud in real-time. Our method is based on a Delaunay triangulation (DT) on the 3D points. The core idea is to robustly label all tetrahedra into freeand occupied space using a random field formulation and to extract the surface as the interface between differently labeled tetrahedra. For this reason, we propose a new energy function that achieves the same accuracy as state-of-the-art methods but reduces the computational effort significantly. Furthermore, our new formulation allows us to extract the surface in an incremental manner, i. e. whenever the point cloud is updated we adapt our energy function. Instead of minimizing the updated energy with a standard graph cut, we employ the dynamic graph cut of Kohli et al. [1] which enables efficient minimization of a series of similar random fields by re-using the previous solution. In such a way we are able to extract the surface from an increasingly growing point cloud nearly independent of the overall scene size. Energy Function for Surface Extraction Our method formulates surface extraction as a binary labeling problem, with the goal of assigning each tetrahedron either a free or occupied label. For this reason, we model the probabilities that a tetrahedron is free- or occupied space analyzing the set of rays that connect all 3D points to image features. Following the idea of the truncated signed distance function (TSDF), which is known from voxel-based surface reconstructions, a tetrahedron in front of a 3D point X has a high probability to be free space, whereas a tetrahedron behind X is presumably occupied space. We further assume that it is very unlikely that neighboring tetrahedra obtain different labels, except for pairs of tetrahedra that have a ray through the face connecting both. Such a labeling problem can be elegantly formulated as a pairwise random field and since our priors are submodular, we can efficiently find a global optimal labeling solution e. g. using graph cuts. In contrast to existing methods like [2], our energy depends only on the visibility information that is directly connected to the four 3D points that span the tetrahedraVi. Hence a modification of the tetrahedral structure by inserting new points has only limited effect on the energy function. This property enables us to easily adopt the energy function to a modified tetrahedral structure. Incremental Surface Extraction To enable efficient incremental surface reconstruction, our method has to consecutively integrate new scene information (3D points as well as visibility information) in the energy function and to minimize the modified energy efficiently. Integrating new visibility information, i. e. adding rays for newly available 3D points, affects only those terms of the energy function that relate
Scalar, electromagnetic and Weyl perturbations of BTZ black holes: Quasinormal modes

We calculate the quasinormal modes and associated frequencies of the Banados-Teitelboim-Zanelli (BTZ) nonrotating black hole. This black hole lives in 2+1 dimensions in an asymptotically anti{endash}de Sitter spacetime. We obtain exact results for the wave function and quasinormal frequencies of scalar, electromagnetic and Weyl (neutrino) perturbations.
Altererythrobacter halimionae sp. nov. and Altererythrobacter endophyticus sp. nov., two endophytes from the salt marsh plant Halimione portulacoides.

Two Gram-negative, rod-shaped, motile bacterial strains, named CPA5T and BR75T, were isolated from the halophyte Halimione portulacoides. Both presented optimum growth at 30 °C, pH 7.0-7.5 and 1-2 % NaCl (w/v) for strain CPA5T, and pH 7.5-8.0 and 2 % NaCl (w/v) for strain BR75T. Phylogenetic analyses based on 16S rRNA gene sequences affiliated both strains to the genus Altererythrobacter. CPA5T presented highest 16S rRNA gene sequence similarity with Altererythrobacter aestuarii KYW147T (96.5 %), followed by Altererythrobacter namhicola KYW48T (95.9 %), Novosphingobium indicum H25T (95.6 %) and Altererythrobacter oceanensis Y2T (95.5 %). BR75T displayed highest similarity with Altererythrobacter marensis MSW-14T (96.5 %), followed by Altererythrobacter xinjiangensis S3-63T, Altererythrobacter luteolus SW-109T and Altererythrobacter indicus MSSRF26T (96.1 %). Neither strain contained Bacteriochlorophyll a. The main fatty acids observed for CPA5T were C17 : 1ω6c and summed features 3 (C16 : 1ω7c and/or iso-C15 : 0 2-OH) and 8 (C18 : 1 ω7c and/or C18 : 1ω6c). The latter summed feature was the dominant fatty acid observed for strain BR75T as well. The major polar lipids were phosphatidylethanolamine, unidentified phospholipids and unidentified glycolipids for both strains. The predominant ubiquinone was Q-10 for both strains, and the DNA G+C contents were 63.4 mol% and 58.3 mol% for CPA5T and BR75T, respectively. Based on phenotypic and genotypic results, both strains represent novel species belonging to the genus Altererythrobacter for which the names Altererythrobacter halimionae sp. nov. (type strain CPA5T=CECT 9130T=LMG 29519T) and Altererythrobacter endophyticus sp. nov (type strain BR75T=CECT 9129T=LMG 29518T) are proposed.
Access, agency and ambiguity: communication about AIDS among young people in Northern Tanzania

Developing interpersonal communication skills is a key component in HIV prevention. Understanding the contexts and content of communication about HIV and AIDS among young people is important for the development of effective programmes and interventions. The aim of this paper is to build on study findings suggesting that significant differences exist between young people in- and out-of-school in exposure to AIDS-related information and communication in Kilimanjaro. In-depth interviews were conducted with young people between the ages of 13 and 18. Data were analyzed using a thematic and comparative approach to explore similarities and differences. A trinity of factors including access, agency and ambiguity emerged as influencing communication about HIV and AIDS. The role of social networks in structuring differences in communication is examined. Findings reveal a discourse laden with fear and mistrust. Coupled with low levels of health literacy, young people regardless of school attendance demonstrate an inability to critically appraise modes of transmission and prevention. An increased emphasis should be placed on the development of critical health literacy and open dialogue. Accessible, unambiguous and culturally relevant interventions built on an understanding of barriers to open communication, which link with community-based initiatives to stimulate dialogue and debate, are needed.
Sibling Transmission of Vaccine-Derived Rotavirus (RotaTeq) Associated With Rotavirus Gastroenteritis

Although rotavirus vaccines are known to be shed in stools, transmission of vaccine-derived virus to unvaccinated contacts resulting in symptomatic rotavirus gastroenteritis has not been reported to our knowledge. We document here the occurrence of vaccine-derived rotavirus (RotaTeq [Merck and Co, Whitehouse Station, NJ]) transmission from a vaccinated infant to an older, unvaccinated sibling, resulting in symptomatic rotavirus gastroenteritis that required emergency department care. Results of our investigation suggest that reassortment between vaccine component strains of genotypes P7[5]G1 and P1A[8]G6 occurred during replication either in the vaccinated infant or in the older sibling, raising the possibility that this reassortment may have increased the virulence of the vaccine-derived virus. Both children remain healthy 11 months after this event and are without underlying medical conditions.
A Hybrid Global-Local Approach for Hierarchical Classification

Hierarchical classification is a variant of multidimensional classification where the classes are arranged in a hierarchy and the objective is to predict a class, or set of classes, according to a taxonomy. Different alternatives have been proposed for hierarchical classification, including local and global approaches. Local approaches are prone to suffer the inconsistency problem, while the global approaches tend to produce more complex models. In this paper, we propose a hybrid globallocal approach inspired on multidimensional classification. It starts by building a local multi-class classifier per each parent node in the hierarchy. In the classification phase all the local classifiers are applied simultaneously to each instance resulting in a most probable class for each classifier. A set of consistent classes are obtained, according to the hierarchy, based on three novel alternatives. The proposed method was tested on three different hierarchical classification data sets and was compared against state-of-the-art methods, resulting in significantly superior performance to the traditional topdown techniques; with competitive results against more complex top-down classifier selection methods.
Exploring categorization property of social annotations for information retrieval

User generated social annotations provide extra information for describing document contents. In this paper, we propose an effective method to model the categorization property of social annotations and explore the potential of combining it with classical language models for improving retrieval performance. Specifically, a novel TR-LDA model is presented to take annotations as an additional source for generating document contents apart from the document itself. We provide strategies for representing and weighting the categorization property and develop an efficient inference algorithm, where space saving is taken into account. Experiments are carried out on synthetic datasets, where documents and queries come from the standard evaluation conference TREC and annotations come from the website Delicious.com. Our results demonstrate the effectiveness of the proposed method on the ad-hoc retrieval task, which significantly outperforms state-of-art baselines.
On the estimation of the marginal density of a moving average process

The authors present a new convolution‐type kernel estimator of the marginal density of an MA(1) process with general error distribution. They prove the √n; ‐consistency of the nonparametric estimator and give asymptotic expressions for the mean square and the integrated mean square error of some unobservable version of the estimator. An extension to MA(q) processes is presented in the case of the mean integrated square error. Finally, a simulation study shows the good practical behaviour of the estimator and the strong connection between the estimator and its unobservable version in terms of the choice of the bandwidth.
Vehicular Communications: A Physical Layer Perspective

Vehicular communications have attracted more and more attention recently from both industry and academia due to their strong potential to enhance road safety, improve traffic efficiency, and provide rich on-board information and entertainment services. In this paper, we discuss fundamental physical layer issues that enable efficient vehicular communications and present a comprehensive overview of the state-of-the-art research. We first introduce vehicular channel characteristics and modeling, which are the key underlying features differentiating vehicular communications from other types of wireless systems. We then present schemes to estimate the time-varying vehicular channels and various modulation techniques to deal with high-mobility channels. After reviewing resource allocation for vehicular communications, we discuss the potential to enable vehicular communications over the millimeter wave bands. Finally, we identify the challenges and opportunities associated with vehicular communications.
The Influence of Autonomy and Paternalism on Communicative Behaviors in Mother-Daughter Relationships Prior to Dependency

Parents and their adult children rarely have discussions regarding caregiving preferences, especially before the onset of dependency. Families develop decision-making practices during caregiving, ideally, ones that maintain the care recipient's autonomy. Maintaining autonomy is essential because limiting autonomy leads to potentially negative health consequences. This study examined the attitudes of older, independent mothers and their adult daughters (n = 36 dyads) as well as their communication behaviors during decision making. All the mothers and daughters (100%) held strong beliefs in shared autonomy. Mothers (55.6%) and daughters (58.3%) were about evenly split in their strength of belief in independent autonomy. More mothers (63.9%) held strong beliefs in paternalism than did daughters (36.1%). In their conversations, daughters talked more when the mother and daughter held stronger beliefs in paternalism. These findings suggest that an individual's attitudes toward paternalism influence who controls conversations between parents and their adult children regarding caregiving, even before the parent has begun to show any signs of dependency. Once caregiving begins, it can be too late to change already ingrained patterns of decision making. Although this study takes a step toward establishing an understanding of how families develop decision-making processes utilized during caregiving, the sample size and composition limit generalizability. Future studies should follow families, as the parents make the transition from independence to dependence, to develop a better understanding of the factors involved in successfully making such a critical transition in the family's life.
A symmetric adaptive algorithm for speeding-up consensus

Performing distributed consensus in a network has been an important research problem for several years, and is directly applicable to sensor networks, autonomous vehicle formation, etc. While there exists a wide variety of algorithms that can be proven to asymptotically reach consensus, in applications involving time-varying parameters and tracking, it is often crucial to reach consensus “as quickly as possible”. In [? ] it has been shown that, with global knowledge of the network topology, it is possible to optimize the convergence time in distributed averaging algorithms via solving a semi-definite program (SDP) to obtain the optimal averaging weights. Unfortunately, in most applications, nodes do not have knowledge of the full network topology and cannot implement the required SDP in a distributed fashion. In this paper, we present a symmetric adaptive weight algorithm for distributed consensus averaging on bi-directional noiseless networks. The algorithm uses an LMS (Least Mean Squares) approach to adaptively update the edge weights used to calculate each node's values. The derivation shows that global error can be minimized in a distributed fashion and that the resulting adaptive weights are symmetric—symmetry being critical for convergence to the true average. Simulations show that convergence time is nearly equal to that of a non-symmetric adaptive algorithm developed in [? ], and significantly better than that of the non-adaptive Metropolis-Hastings algorithm. Most importantly, our symmetric adaptive algorithm converges to the sample mean, whereas the method of [? ] converges to an arbitrary value and results in significant error.
Perceived Gender Atypicality on High and Low Gender Role Rigidity

Perceived Gender Atypicality on High and Low Gender Role Rigidity. (April 2010) Aimee Marie Howarth Department of Psychology Texas A&M University Research Co-Advisors: Dr. Gerianne Alexander and Nora Charles Department of Psychology The objective of this research was to study the physiological, emotional, and cognitive response to gender role threats in individuals with high and low gender role rigidity. Participants were selected based on their responses to the Masculine and Feminine Gender Role Stress scale (MGRS &FGRS) completed during a pre-screening session. At the test session, participants (41 men and 45 women) completed the Bem Sex Role Inventory (BSRI) and a mating preferences questionnaire using the 13 criteria from Buss and Barnes that measured the strength of self-identification with traditionally masculine and feminine gender roles, levels of stress with their reported gender role, and preferences for prospective partners’ gender-related traits and behaviors. After completing the questionnaire measures, participants viewed 20 pictures presented on a computer monitor depicting typical and atypical female gender role job professions (e.g. nurse and a construction worker), as well as typical and atypical male gender role job