Miracle-dz/UniFAR-FTUs huggingface dataset:
Maternal and child reflective functioning in the context of child sexual abuse: pathways to depression and externalising difficulties

Background Sexual abuse is a well-recognised risk factor for child psychopathology. Little is known regarding whether child and maternal mentalization can be considered a potential resource or protective factor in this context, respectively, mediating or moderating the relationship between sexual abuse and psychopathology. Objective The aims of this study were (1) to explore the relationships between child and maternal mentalizing, measured as reflective functioning (RF), and child depressive symptoms and externalising difficulties; and (2) to examine whether child mentalizing mediates the relationship between child sexual abuse (CSA) and psychopathology. Method A total of 168 children aged 7–12 years and their mothers participated in the study. The sample included 74 dyads where children had experienced sexual abuse. The Child Attachment Interview was rated by using the Child Reflective Functioning Scale to assess children's mentalization, and the Child Depression Inventory was used to assess depressive symptoms. Mothers completed the Parent Development Interview to assess maternal RF and the Child Behavior Checklist to assess their child's externalising difficulties. A model involving direct and indirect paths from CSA, child and maternal RF to child psychopathology was examined using Mplus software. Results Child mentalization partially mediated the relationships between CSA and depressive symptoms, as well as the relationship between CSA and externalising difficulties. Maternal mentalization was an independent predictor of child externalising difficulties, with higher maternal RF associated with less externalising difficulties. Discussion The findings indicate that by ages 7–12, child mentalization is an important inner resource associated with lower depression and externalising. In addition, this study provides new evidence of the importance of the parent's mentalizing stance for the development of self-regulation and externalising difficulties in both abused and non-abused children. The clinical implications are discussed.
Effects of Recombinant Human Thyrotropin Administration on 24-Hour Arterial Pressure in Female Undergoing Evaluation for Differentiated Thyroid Cancer

Objective. Thyroid-stimulating-hormone (TSH) receptors are expressed in endothelial cells. We investigated whether elevated TSH levels after acute recombinant TSH (rhTSH) administration may result in alterations in blood pressure (BP) in premenopausal women with well-differentiated thyroid carcinoma (DTC). Designs. Thirty euthyroid DTC female patients were evaluated by rhTSH stimulation test (mean age 40.4 ± 8.6 years). A 24 h ambulatory systolic and diastolic blood pressure (SBP, DBP) monitoring (24 hr ABPM) was performed on days 2-3(D2-3). TSH was measured on day 1(D1), day 3(D3), and day 5(D5). Central blood pressure was evaluated on D3. Twenty-three patients were studied 1–4 weeks earlier (basal measurements). Results. TSH levels were D1: median 0.2 mU/L, D3: median 115.0 mU/L, and D5: median 14.6 mU/L. There were no significant associations between TSH on D1 and D3 and any BP measurements. Median D5 office-SBP and 24 h SBP, DBP, and central SBP were correlated with D5-TSH (P < 0.04). In those where a basal 24 h ABPM had been performed median pulse pressure was higher after rhTSH-test (P = 0.02). Conclusions. TSH, when acutely elevated, may slightly increase SBP, DBP, and central SBP. This agrees with previous reports showing positive associations of BP with TSH.
Sensory load hierarchy-based classification of schizophrenia patients

Schizophrenia is currently diagnosed by physicians through evaluation of their clinical assessment and a patient's self-reported experience over the longitudinal course of the illness. There is great interest in identifying biologically based markers at illness onset, rather than relying on the evolution of symptoms across time. Functional connectivity shows promise in providing individual subject predictive power. However, the majority of previous studies considered only the analysis of functional connectivity during resting-state or performance of a single task. Changes in connectivity between rest and multiple tasks have not been used in the discrimination of schizophrenia patients from healthy controls. In this work, we propose a framework for classification of schizophrenia patients and healthy control subjects based on functional network component pairs which show consistency between patients and controls across levels of the resting-state data and task hierarchy. Our results show that these functional network components as a function of task contain valuable information for individual prediction of schizophrenia patients. Such information is useful for training and replicates in testing. Performance was improved significantly (up to ~20%) relative to a single FNC (resting-state) measure.
Fusion of GPS and image data for accurate geocoding of street-level fisheye images

Geospatial tools and techniques are becoming more important for land surveyors to do their off-location inspections of the urban areas. Accurate geocoded street-level images are the base of these tools. For these applications, an error of 2.5 meters is tolerable. However, the geographic coordinates provided by GPS have error up to 10 meters. In this paper we propose an automatic method to improve the accuracy of geocoding of street-level images by registering them to the accurate geocoded reference image, which is the satellite image. The proposed technique uses an unconstrained nonlinear optimization method to find local optimal solutions by matching high-level features and their relative locations. A global optimization method is then employed over all of the local solutions by applying a geometric constraint. We used our algorithm for correcting the geographic information of more than 2500 fisheye images and show that the proposed algorithm can achieve an average error of 1.19 meters along both x and y directions.
Enhancing location prediction with big data: evidence from dhaka

In recent years, the study of location prediction has received heightened attention due to its applications in LBS and other areas. However, most of the techniques and subsequent conclusions drawn from previous research works are specific to the data used in the study. For instance, resolution of location data and inclusion of external data (e.g., from social networks) may limit application of previous techniques to new situations. Therefore, we explore ways of enhancing location prediction techniques which leverage big data without the need for external data sources. To this end, we study a large CDR dataset with more than 3.5 billion calls from a leading cellular network provider in Dhaka, Bangladesh. The research question we tackle is how we can leverage big data to enhance performance of location predictors? Based on spatio-temporal analysis of call activity, we devise a scheme to compute prior probabilities from cell call activity. With this reasoning, we develop an enhanced Bayes predictor which uses a distance threshold and the users' regular location to improve generation of prior probabilities. Experimental results show that overall the enhanced Bayes predictor improves accuracy by 17 percentage points.
Afﬁne shape analysis and image analysis

study of afﬁne shape is needed in certain problems that arise in bioinformatics and pattern recognition. In particular afﬁne shape analysis is useful in the analysis of 2D electrophoresis images and in the reconstruction of a larger area from a number aerial images. Two aerial images taken from different distances of a ground scene are given in Figure 1. ; the problem asks to reconstruct a larger contiguous image that contains the information in both these images
Risk-Association of CYP11A1 Polymorphisms and Breast Cancer Among Han Chinese Women in Southern China

Exposure to endogenous sex hormones has been reported as a risk factor for breast cancer. The CYP11A1 gene encodes the key enzyme that catalyzes the initial and rate-limiting step in steroid hormone synthesis. In this study, the associations between single nucleotide polymorphisms (SNPs) in CYP11A1 and breast cancer susceptibility were examined. Six SNPs in CYP11A1 were genotyped using the MassARRAY IPLEX platform in 530 breast cancer patients and 546 healthy controls. Association analyses based on a χ2 test and binary logistic regression were performed to determine the odds ratio (OR) and 95% confidence interval (95% CI) for each SNP. Two loci (rs2959008 and rs2279357) showed evidence of associations with breast cancer risk. The variant genotype C/T-C/C of rs2959008 was significantly associated with a decreased risk (age-adjusted OR, 0.75; 95% CI, 0.58–0.96; P = 0.023) compared with the wild-type TT. However, the homozygous TT variant of rs2279357 exhibited increased susceptibility to breast cancer (age-adjusted OR, 1.44; 95% CI, 1.05–1.98; P = 0.022). The locus rs2959003 also showed an appreciable effect, but no associations were observed for three other SNPs. Our results suggest that polymorphisms of CYP11A1 are related to breast cancer susceptibility in Han Chinese women of South China.
Deep belief net learning in a long-range vision system for autonomous off-road driving

We present a learning-based approach for long-range vision that is able to accurately classify complex terrain at distances up to the horizon, thus allowing high-level strategic planning. A deep belief network is trained with unsupervised data and a reconstruction criterion to extract features from an input image, and the features are used to train a realtime classifier to predict traversability. The online supervision is given by a stereo module that provides robust labels for nearby areas up to 12 meters distant. The approach was developed and tested on the LAGR mobile robot.
A comparison of linear skinning techniques for character animation

Character animation is the task of moving a complex, artificial character in a life-like manner. A widely used method for character animation involves embedding a simple skeleton within a character model and then animating the character by moving the underlying skeleton. The character's skin is required to move and deform along with the skeleton. Research into this problem has resulted in a number of skinning frameworks. There has, however, been no objective attempt to compare these methods. We compare three linear skinning frameworks that are computationally efficient enough to be used for real-time animation: Skeletal Subspace Deformation, Animation Space and Multi-Weight Enveloping. These create a correspondence between the points on a character's skin and the underlying skeleton by means of a number of weights, with more weights providing greater flexibility. The quality of each of the three frameworks is tested by generating the skins for a number of poses for which the ideal skin is known. These generated skin meshes are then compared to the ideal skins using various mesh comparison techniques and human studies are used to determine the effect of any temporal artefacts introduced. We found that Skeletal Subspace Deformation lacks flexibility while Multi-Weight Enveloping is prone to overfitting. Animation Space consistently outperforms the other two frameworks.
Survival of Developing Motor Neurons Mediated by Rho GTPase Signaling Pathway through Rho-Kinase

A variety of neurons generated during embryonic development survive or undergo programmed cell death (PCD) at later developmental stages. Survival or death of developing neurons is generally considered to depend on trophic support from various target tissues. The small GTPase Rho regulates diverse cellular processes such as cell morphology, cell adhesion, cell motility, and apoptosis. Rho-dependent serine–threonine protein kinase (Rho-kinase–ROK–ROCK), one of the effector proteins, transmits signals for some Rho-mediated processes. Here, we report the in vivo role of the Rho signaling pathway through Rho-kinase during development of motor neurons (MNs) in the spinal cord. We performed conditional expression of a dominant-negative form for RhoA (RhoA DN) or for Rho-kinase (Rho-K DN) in transgenic mice by using the Cre-loxP system to suppress the activity of these signaling molecules in developing MNs. Expression of RhoA DN reduced the number of MNs in the spinal cord because of increased apoptosis while preserving the gross patterning of motor axons. Expression of Rho-K DN produced developmental defects similar to those observed in RhoA DN expression. In addition, analysis of transgenic mice expressing Rho-K DN showed that the increased apoptosis of MNs was induced at the early embryonic stages before the initiation of PCD, and that MN death at the late embryonic stages corresponding to the period of PCD was moderately enhanced in the transgenic mice. These findings indicate that the Rho signaling pathway, primarily through Rho-kinase, plays a crucial role in survival of spinal MNs during embryogenesis, particularly at the early developmental stages.
People tracking using synthetically generated depth maps

Modern 3D sensors using time-of-flight and structured light technology are increasingly being used in a number of interesting applications. One such application is people tracking where the available 3D information enables more robust human detection and tracking methods than those based on classic video cameras. A large data set is typically required both for testing and tuning of the solution. We describe a methodology for efficient development of 3D applications using synthetically generated depth maps to complement the available data set obtained with real cameras. To demonstrate the effectiveness of the approach, a case study in the area of people counting with 3D sensors is presented.
Metabolomics Identifies Multiple Candidate Biomarkers to Diagnose and Stage Human African Trypanosomiasis

Treatment for human African trypanosomiasis is dependent on the species of trypanosome causing the disease and the stage of the disease (stage 1 defined by parasites being present in blood and lymphatics whilst for stage 2, parasites are found beyond the blood-brain barrier in the cerebrospinal fluid (CSF)). Currently, staging relies upon detecting the very low number of parasites or elevated white blood cell numbers in CSF. Improved staging is desirable, as is the elimination of the need for lumbar puncture. Here we use metabolomics to probe samples of CSF, plasma and urine from 40 Angolan patients infected with Trypanosoma brucei gambiense, at different disease stages. Urine samples provided no robust markers indicative of infection or stage of infection due to inherent variability in urine concentrations. Biomarkers in CSF were able to distinguish patients at stage 1 or advanced stage 2 with absolute specificity. Eleven metabolites clearly distinguished the stage in most patients and two of these (neopterin and 5-hydroxytryptophan) showed 100% specificity and sensitivity between our stage 1 and advanced stage 2 samples. Neopterin is an inflammatory biomarker previously shown in CSF of stage 2 but not stage 1 patients. 5-hydroxytryptophan is an important metabolite in the serotonin synthetic pathway, the key pathway in determining somnolence, thus offering a possible link to the eponymous symptoms of “sleeping sickness”. Plasma also yielded several biomarkers clearly indicative of the presence (87% sensitivity and 95% specificity) and stage of disease (92% sensitivity and 81% specificity). A logistic regression model including these metabolites showed clear separation of patients being either at stage 1 or advanced stage 2 or indeed diseased (both stages) versus control.
BreDoBrothers Team Description for RoboCup 2006

1 Deutsches Forschungsinstitut für Künstliche Intelligenz GmbH, Sichere Kognitive Systeme, Robert-Hooke-Str. 5, 28359 Bremen, Germany E-Mail: Thomas.Roefer@dfki.de 2 Robotics Research Institute, Dortmund University, Otto-Hahn-Str. 8, 44227 Dortmund, Germany. E-Mail: {forename.surname }@uni-dortmund.de 3 Faculty 3 Mathematics / Computer Science, Universität Bremen, Postfach 330 440, 28334 Bremen, Germany. E-Mail: {fritsche,timlaue,cniehaus,harlekin}@tzi.de
Structural Equation Modeling in a Rationalization Tentative of Balanced Scorecard

This paper examines the relationship between structural equation modeling and the balanced scorecard in a health care institution. Using financial and non-financial data taken from the social-medical establishment, the paper propose a rational construction of Balanced Scorecard by choosing the right indicators for the right axes. This choice is made by implementing the Partial Least Squares (PLS) in our model. In addition, the scheme gives us the cause-and-effect chain, the one described by Kaplan and Norton as : measures of organizational learning and growth - measures of internal business processes - measures of the customer perspective - financial measures. We will observe that is a health care institution the cause-and-effect chain will change as the primordial goal for a medical-social establishment is not maximizing profitability, but maximizing patient satisfaction. At the end of our article we will proceed to a comparison between two SEM methods : Covariance Based Method (CBM) and Partial Least Squares (PLS).
Generalized Gradient Learning on Time Series under Elastic Transformations

The majority of machine learning algorithms assumes that objects are represented as vectors. But often the objects we want to learn on are more naturally represented by other data structures such as sequences and time series. For these representations many standard learning algorithms are unavailable. We generalize gradient-based learning algorithms to time series under dynamic time warping. To this end, we introduce elastic functions, which extend functions on time series to matrix spaces. Necessary conditions are presented under which generalized gradient learning on time series is consistent. We indicate how results carry over to arbitrary elastic distance functions and to sequences consisting of symbolic elements. Specifically, four linear classifiers are extended to time series under dynamic time warping and applied to benchmark datasets. Results indicate that generalized gradient learning via elastic functions have the potential to complement the state-of-the-art in statistical pattern recognition on time series.
Music Recommendation System for Public Places Based on Sensor Network

Summary Recently, there have been several studies about personal music recommendation system on the internet and personal computer environment, but the music recommendation system for public places has not been studied. In this paper, we study a music recommendation system based on a sensor network in public places such as highway rest places, parks, huge marts, etc. The proposed system includes five modules consisting of sensor nodes, sink nodes, host, music database, and remote control module. Sensor nodes are installed at places where people are coming and going frequently. The information sensed by sensor nodes is transmitted to the host through sink nodes using RF communication, and then the host computes the density of people from this information. In order to choose a music that best matches the current situation from our music database, several variables such as the density of people, season, weather, and time are used as main parameters. We also evaluate and analyze the accuracy of this system through an experiment environment.
Mean square error reduction by precoding of mixed Gaussian input

Suppose a vector of observations y = Hx+n stems from independent inputs x and n, both of which are Gaussian Mixture (GM) distributed, and that H is a fixed and known matrix. This work focuses on the design of a precoding matrix, F, such that the model modifies to z = HFx + n. The goal is to design F such that the mean square error (MSE) when estimating x from z is smaller than when estimating x from y. We do this under the restriction E[(Fx)TFx] ≤ PT, that is, the precoder cannot exceed an average power constraint. Although the minimum mean square error (MMSE) estimator, for any fixed F, has a closed form, the MMSE does not under these settings. This complicates the design of F. We investigate the effect of two different precoders, when used in conjunction with the MMSE estimator. The first is the linear MMSE (LMMSE) precoder. This precoder will be mismatched to the MMSE estimator, unless x and n are purely Gaussian variates. We find that it may provide MMSE gains in some setting, but be harmful in others. Because the LMMSE precoder is particularly simple to obtain, it should nevertheless be considered. The second precoder we investigate, is derived as the solution to a stochastic optimization problem, where the objective is to minimize the MMSE. As such, this precoder is matched to the MMSE estimator. It is derived using the KieferWolfowitz algorithm, which moves iteratively from an initially chosen F0 to a local minimizer F*. Simulations indicate that the resulting precoder has promising performance.
A Game-Theoretic Analysis of the ESP Game ∗ PRELIMINARY DRAFT : COMMENTS WELCOME

In recent years, there has been a great deal of progress in “Games with a Purpose,” interactive games that users play because they are fun, with the added benefit that they are doing useful work in the process. The ESP game, developed by von Ahn and Dabbish [24], is an example of such a game devised to label images on the web. Since labeling images is a hard problem for computer vision algorithms and can be tedious and time-consuming for humans, the ESP game provides humans with incentive to do useful work by being enjoyable to play. We present a simple game-theoretic model of the ESP game and characterize the equilibrium behavior in our model. Our equilibrium analysis supports the fact that users appear to be coordinating on low effort words. We provide an alternate model of utility and show that equilibrium behavior in this model achieves more desirable outcomes, from the system designer’s perspective. We also give sufficient conditions for coordinating on high effort words to be a BayesianNash equilibrium. Our results suggest the possibility of formal incentive design in achieving desirable system-wide outcomes in this area of “human computation” in complementing existing considerations of robustness against cheating and human factors.
D = 7 selfdual string in the 5-brane

Unlike the Schwarzschild black string in the Randall-Sundrum scenario which is known to have the geodesics reaching the AdS-horizon terminating there, the D=7 extremal BPS selfdual string of N = 2 gauged supergravity potentially differs from this result. I give a complete proof that timelike radial trajectories of the selfdual string solution that escapes to r = ∞ do not see a curvature singularity on the horizon at z = ∞.
Friction Identification Based Upon the LuGre and Maxwell Slip Models

The problem of friction identification within the presliding and sliding regimes is addressed and three identification methods, designated as the LuGre (LG) method, the nonlinear regression (NLR) method, and dynamic nonlinear regression with direct application of the excitation (DNLRX) method, are postulated. The first employs the LG model structure, the second the basic Maxwell slip model structure, and the third an extended version of it. The Maxwell Slip model structure accounts for the presliding hysteresis with nonlocal memory, but is confined to providing constant sliding friction. This limitation is circumvented by the postulated extended version. In all methods identification is based upon signals obtained from a single experiment. The methods are successfully assessed via Monte Carlo experiments, as well as via a laboratory setup. The DNLRX is shown to achieve the best overall performance, followed by the NLR and LG methods. A simple DNLRX-based feedforward friction compensation scheme is also postulated and assessed. The results indicate that it is capable of yielding effective friction compensation.