Miracle-dz/UniFAR-FTUs huggingface dataset:
Occurrence of ADHD in parents of ADHD children in a clinical sample

Background Despite the fact that there is a large amount of research on childhood attention deficit hyperactivity disorder (ADHD) treatment and an increasing amount of research on adult ADHD, little is known about the prevalence and influence of parental ADHD. Therefore, this study examined the frequency of parental ADHD in a clinical sample of German children suffering from ADHD. We also tried to find different levels of symptom severity for prognostic relevance. Furthermore, the association between subtypes of ADHD in children and their parents was investigated. Method In this study, parents of 79 ADHD children were screened for ADHD according to the Diagnostic and Statistical Manual of Mental Disorders, 5th edition and International Classification of Diseases, 10th edition. The Wender Utah Rating Scale and the ADHS-Self-Report were given to 75 mothers and 49 fathers for retrospective and current symptoms. Frequency of ADHD symptoms and severity groups was calculated and relationship between parental and children’s ADHD was tested. Results ADHD occurrence for mothers of children with ADHD was 41.3%, for fathers 51.0%. About 16.0% of the mothers had a mixed type, 9.3% had a hyperactive-impulsive subtype, and 16.0% had an inattentive subtype. Of the fathers, 18.4% had a mixed type, 10.2% had a hyperactive-impulsive subtype, and 22.4% had an inattentive subtype; 61% of the mothers and 46.9% of the fathers had low symptom severity. Medium symptom severity was reported by 37.7% mothers and 46.9% fathers, while 1.3% of the mothers and 6.2% of the fathers showed severe symptoms. No significant correlation between parental and child diagnoses was observed. Conclusion As nearly half of the parents suffered from ADHD, these results are a matter of concern in families with ADHD children. Besides parent–child interactions, parental ADHD symptoms might influence parental education style and also effects parent training as well as the child’s therapy outcome. In the future, parents should be screened for ADHD symptoms if they or their child receive treatment and to adjust processes and design of treatment to the symptoms.
Computing Exact Skyline Probabilities for Uncertain Databases

With the rapid increase in the amount of uncertain data available, probabilistic skyline computation on uncertain databases has become an important research topic. Previous work on probabilistic skyline computation, however, only identifies those objects whose skyline probabilities are higher than a given threshold, or is useful only for 2D data sets. In this paper, we develop a probabilistic skyline algorithm called PSkyline which computes exact skyline probabilities of all objects in a given uncertain data set. PSkyline aims to identify blocks of instances with skyline probability zero, and more importantly, to find incomparable groups of instances and dispense with unnecessary dominance tests altogether. To increase the chance of finding such blocks and groups of instances, PSkyline uses a new in-memory tree structure called Z-tree. We also develop an online probabilistic skyline algorithm called O-PSkyline for uncertain data streams and a top-k probabilistic skyline algorithm called K-PSkyline to find top-k objects with the highest skyline probabilities. Experimental results show that all the proposed algorithms scale well to large and high-dimensional uncertain databases.
Natural scene recognition based on Convolutional Neural Networks and Deep Boltzmannn Machines

Scene recognition is a significant topic in computer vision, and Deep Boltzmann Machines (DBM) is a state-of-the-art deep learning model which has been widely applied in object and hand written digit recognition. However, when the DBM is used in scene recognition, it is difficult to handle large images due to its computational complexity. In this paper, we present a deep learning method based on Convolutional Neural Networks (CNN) and DBM for scene image recognition. First, in order to categorize large images, the CNN is utilized to preprocess images for dimensional reduction. Then, regarding the preprocessed images as the input of the visible layer, the DBM model is trained using Contrastive Divergence (CD) algorithm. Finally, after extracting features by the DBM, the softmax regression is employed to perform scene recognition tasks. Since the CNN can reduce effectively image size, the proposed method can improve the computational efficiency and becomes more suitable for large image recognition. Experimental evaluations using SIFT Flow dataset and fifteen-scene dataset demonstrate that the proposed method can obtain promising results.
Effects of clouds on surface melting of Laohugou glacier No. 12, western Qilian Mountains, China

ABSTRACT We analyzed a 2-year time series of meteorological data (January 2011–December 2012) from three automatic weather stations on Laohugou glacier No. 12, western Qilian Mountains, China. Air temperature, humidity and incoming radiation were significantly correlated between the three sites, while wind speed and direction were not. In this work, we focus on the effects of clouds on other meteorological parameters and on glacier melt. On an average, ~18% of top-of-atmosphere shortwave radiation was attenuated by the clear-sky atmosphere, and clouds attenuated a further 12%. Most of the time the monthly average increases in net longwave radiation caused by clouds were larger than decreases in net shortwave radiation but there was a tendency to lose energy during the daytime when melting was most intense. Air temperature and wind speed related to turbulent heat flux were found to suppress glacier melt during cloudy periods, while increased water vapor pressure during cloudy days could enhance glacier melt by reducing energy loss by latent heat. From these results, we have increased the physical understanding of the significance of cloud effects on continental glaciers.
Optimal Utility-Privacy Trade-off with the Total Variation Distance as the Privacy Measure

The total variation distance is proposed as a privacy measure in an information disclosure scenario when the goal is to reveal some information about available data in return of utility, while retaining the privacy of certain sensitive latent variables from the legitimate receiver. The total variation distance is introduced as a measure of privacy-leakage by showing that: i) it satisfies the post-processing and linkage inequalities, which makes it consistent with an intuitive notion of a privacy measure; ii) the optimal utility-privacy trade-off can be solved through a standard linear program when total variation distance is employed as the privacy measure; iii) it provides a bound on the privacy-leakage measured by mutual information, maximal leakage, or the improvement in an inference attack with a bounded cost function.
Flexible shortcuts: designing a new speech user interface for command execution

This paper proposes a new speech user interface for command execution called "Flexible Shortcuts. " With this approach, users can select any commands by using "continuous keyword input," a voice input method using a series of keywords related to the command. Keywords are defined by a hierarchically structured command set, called the "functional structure. " Two kinds of interactions are designed to support user-friendliness and effectiveness: interaction for exploration and interaction for the resolution of ambiguity. A probabilistic formulation for this approach is also considered. An experiment and objective evaluation method are designed so that the new interface for command execution can be compared with the conventional "command and control" interface. A comparative experiment to measure the efficiency and usability is also conducted and reported in this paper. Experimental results show that the proposed approach is superior to the conventional approach from both objective and subjective points of view.
A study on regularization effects of fuzzified memberships in FCM clustering

FCM clustering is a fundamental technique for capturing intrinsic cluster structures of multivariate data sets. This paper presents a comparative study on the regularization effects of Fuzzy c-Means memberships estimated by two different fuzzification approaches: standard approach and entropy reg-ularization approach. In this paper, the characteristics of the two fuzzification approaches are also discussed in noise fuzzy clustering (NFC) and it is revealed that the noise rejection mechanism of NFC can contribute to weakening the influence of initialization problems in entropy regularization approach although the approach is generally more sensitive to initial partition than the standard approach.
On Nearest-Neighbor Error-Correcting Output Codes with Application to All-Pairs Multiclass Support Vector Machines

A common way of constructing a multiclass classifier is by combining the outputs of several binary ones, according to an error-correcting output code (ECOC) scheme. The combination is typically done via a simple nearest-neighbor rule that finds the class that is closest in some sense to the outputs of the binary classifiers. For these nearest-neighbor ECOCs, we improve existing bounds on the error rate of the multiclass classifier given the average binary distance. The new bounds provide insight into the one-versus-rest and all-pairs matrices, which are compared through experiments with standard datasets. The results also show why elimination (also known as DAGSVM) and Hamming decoding often achieve the same accuracy.
Data-intensive image based relighting

Image based Relighting(IBRL) has attracted a lot of interest in the computer graphics research, gaming, and virtual cinematography communities for its ability to relight objects or scenes, from novel illuminations captured in natural or synthetic environments. However, the advantages of an image-based framework conflicts with a drastic increase in the storage caused by the huge number of reference images pre-captured under various illumination conditions. To perform fast relighting, while maintaining the visual fidelity, one needs to preprocess this huge data into an appropriate model. In this paper, we propose a novel and efficient two-stage relighting algorithm which creates a compact representation of the huge IBRL dataset and facilitates fast relighting. In the first stage, using Singular Value Decomposition, a set of eigen image bases and relighting coefficients are computed. In the second stage, and in contrast to prior methods, the correlation among the relighting coefficients is harnessed using Spherical Harmonics. The proposed method thus has lower memory and computational requirements. We demonstrate our results qualitatively and quantitatively with new generated image data.
Dynamic Vehicle Localization using Constraints Propagation Techniques on Intervals A comparison with Kalman Filtering

In order to implement a continuous and robust dynamic localization of a mobile robot, the fusion of dead reckoning and absolute sensors is often used. Depending on the objectives of precision or integrity, the choice of an algorithm could be crucial. For example, if the models used for the fusion are non linear, classical tools (such as a Kalman filter) cannot guarantee maximum error estimation. There are bounded error approaches that are insensitive to non linearity. In this context, the random errors are only modeled by their maximum bound. This paper compares a technique based on constraints propagation on intervals, with the usual Extended Kalman Filter for the data fusion of redundant sensors. We have thus developed both techniques and we consider the fusion of wheel encoders, a gyro and a differential GPS receiver. Experimental results show that the precision of a constraints propagation technique can be very good with guaranteed estimations. Moreover, such an approach is well adapted to a real time implementation.
Arabidopsis Microtubule-Associated Protein MAP65-3 Cross-Links Antiparallel Microtubules toward Their Plus Ends in the Phragmoplast via Its Distinct C-Terminal Microtubule Binding Domain[W]

MAP65-3 contains a distinct C-terminal microtubule binding site that is not shared by other Arabidopsis MAP-65 proteins. This study demonstrates that this C-terminal extension determines the protein’s specific function in cross-linking antiparallel microtubules in the phragmoplast midzone. Plant cytokinesis is brought about by the phragmoplast, which contains an antiparallel microtubule (MT) array. The MT-associated protein MAP65-3 acts as an MT-bundling factor that specifically cross-links antiparallel MTs near their plus ends. MAP65 family proteins contain an N-terminal dimerization domain and C-terminal MT interaction domain. Compared with other MAP65 isoforms, MAP65-3 contains an extended C terminus. A MT binding site was discovered in the region between amino acids 496 and 588 and found to be essential for the organization of phragmoplast MTs. The frequent cytokinetic failure caused by loss of MAP65-3 was not rescued by ectopic expression of MAP65-1 under the control of the MAP65-3 promoter, indicating nonoverlapping functions between the two isoforms. In the presence of MAP65-3, however, ectopic MAP65-1 appeared in the phragmoplast midline. We show that MAP65-1 could acquire the function of MAP65-3 when the C terminus of MAP65-3, which contains the MT binding site, was grafted to it. Our results also show that MAP65-1 and MAP65-3 may share redundant functions in MT stabilization. Such a stabilization effect was likely brought about by MT binding and bundling. We conclude that MAP65-3 contains a distinct C-terminal MT binding site with a specific role in cross-linking antiparallel MTs toward their plus ends in the phragmoplast.
An Experience in Image Compression Using Neural Networks

There are number trials of using neural networks as signal processing tools for image compression. In this paper, a direct solution method is used for image compression using the neural networks. An experience of using multilayer perceptron for image compression is presented. The multilayer perceptron is used for transform coding of the image. The network is trained for different number of hidden neurons with direct impact to compress ratio. It is experimented with different images that have been segmented in the blocks of various sizes for compression process. Reconstructed image is compared with original image using signal-to-noise ratio and number of bits per pixel. The results show the possibility of using multilayer perceptrons for image compression
Vertical partitioning of phosphate uptake among picoplankton groups in the low Pi Mediterranean Sea

Abstract. Microbial transformations are key processes in marine phosphorus cycling. In this study, we investigated the contribution of phototrophic and heterotrophic groups to phosphate (Pi) uptake fluxes in the euphotic zone of the low-Pi Mediterranean Sea and estimated Pi uptake kinetic characteristics. Surface soluble reactive phosphorus (SRP) concentrations were in the range of 6–80 nmol L−1 across the transect, and the community Pi turnover times, assessed using radiolabeled orthophosphate incubations, were longer in the western basin, where the highest bulk and cellular rates were measured. Using live cell sorting, four vertical profiles of Pi uptake rates were established for heterotrophic prokaryotes (Hprok), phototrophic picoeukaryotes (Pic) and Prochlorococcus (Proc) and Synechococcus (Syn) cyanobacteria. Hprok cells contributed up to 82% of total Pi uptake fluxes in the superficial euphotic zone, through constantly high abundances (2.7–10.2 × 105 cells mL−1) but variable cellular rates (6.6 ± 9.3 amol P cell−1 h−1). Cyanobacteria achieved most of the Pi uptake (up to 62%) around the deep chlorophyll maximum depth, through high abundances (up to 1.4 × 105 Proc cells mL−1) and high cellular uptake rates (up to 40 and 402 amol P cell−1 h−1, respectively for Proc and Syn cells). At saturating concentrations, maximum cellular rates up to 132 amol P cell−1 h−1 were measured for Syn at station (St.) C, which was 5 and 60 times higher than Proc and Hprok, respectively. Pi uptake capabilities of the different groups likely contribute to their vertical distribution in the low Pi Mediterranean Sea, possibly along with other energy limitations.
Universal similarity

We survey a new area of parameter-free similarity distance measures useful in data-mining, pattern recognition, learning and automatic semantics extraction. Given a family of distances on a set of objects, a distance is universal up to a certain precision for that family if it minorizes every distance in the family between every two objects in the set, up to the stated precision (we do not require the universal distance to be an element of the family). We consider similarity distances for two types of objects: literal objects that as such contain all of their meaning, like genomes or books, and names for objects. The latter may have literal embodyments like the first type, but may also be abstract like "red" or "christianity:' For the first type we consider a family of computable distance measures corresponding to parameters expressing similarity according to particular features between pairs of literal objects. For the second type we consider similarity distances generated by Web users corresponding to particular semantic relations between the (names for) the designated objects. For both families we give universal similarity distance measures, incorporating all particular distance measures in the family. In the first case the universal distance is based on compression and in the second case it is based on Google page counts related to search terms. In both cases experiments on a massive scale give evidence of the viability of the approaches.
A Proposed Mechanism for the Interaction of the Segmentation Clock and the Determination Front in Somitogenesis

Background Recent discoveries in the field of somitogenesis have confirmed, for the most part, the feasibility of the clock and wavefront model. There are good candidates for both the clock (various genes expressed cyclically in the tail bud of vertebrate embryos have been discovered) and the wavefront (there are at least three different substances, whose expression levels vary along the presomitic mesoderm [PSM], that have important effects on the formation of somites). Nevertheless, the mechanisms through which the wavefront interacts with the clock to arrest the oscillations and induce somite formation have not yet been fully elucidated. Principal Findings In this work, we propose a gene regulatory network which is consistent with all known experimental facts in embryonic mice, and whose dynamic behaviour provides a potential explanation for the periodic aggregation of PSM cells into blocks: the first step leading to the formation of somites. Significance To our knowledge, this is the first proposed mechanism that fully explains how a block of PSM cells can stop oscillating simultaneously, and how this process is repeated periodically, via the interaction of the segmentation clock and the determination front.
Schemes for scheduling of control messages by hierarchical protocols

The paper addresses the problem of designing efficient scheduling policies for the transmission of control messages by hierarchical network protocols. Such protocols encounter a tradeoff between the desire to forward a control message across the tree as soon, as it is received, and the desire to reduce control traffic. Scheduling problems that arise in this context are defined and discussed. The paper mainly concentrates on minimizing the average extra delay encountered by the control messages under an upper bound on the number of outgoing messages a node can send during a fixed period of time. A polynomial-time algorithm is presented for the off-line version of the problem, and then several efficient on-line heuristics are presented and compared.
Image-based X-ray visualization techniques for spatial understanding in outdoor augmented reality

This paper evaluates different state-of-the-art approaches for implementing an X-ray view in Augmented Reality (AR). Our focus is on approaches supporting a better scene understanding and in particular a better sense of depth order between physical objects and digital objects. One of the main goals of this work is to provide effective X-ray visualization techniques that work in unprepared outdoor environments. In order to achieve this goal, we focus on methods that automatically extract depth cues from video images. The extracted depth cues are combined in ghosting maps that are used to assign each video image pixel a transparency value to control the overlay in the AR view. Within our study, we analyze three different types of ghosting maps, 1) alpha-blending which uses a uniform alpha value within the ghosting map, 2) edge-based ghosting which is based on edge extraction and 3) image-based ghosting which incorporates perceptual grouping, saliency information, edges and texture details. Our study results demonstrate that the latter technique helps the user to understand the subsurface location of virtual objects better than using alpha-blending or the edge-based ghosting.
Design and Optimization of Cloud-Oriented Workflow System

The paper firstly analyzes the characteristic of existing workflow applications and proposes cloud workflow layered model and design method based on the current major cloud computing trends. Considering the features on the cloud workflow system in distributed heterogeneous environment, the paper proposes a brief introduction to approach to cloud workflow system. Next, three deployment models of the cloud workflow applications are presented. Finally, the paper presents a new dynamic scheduling algorithm –Improved Dynamic Cloud Scheduling Algorithm(IDCSA) which takes consideration of the characteristics of cloud workflow and cloud resources.