Miracle-dz/UniFAR-FTUs huggingface dataset:
DACA: Dynamic Advanced Clustering Algorithm for Sensor Networks

The clustering of sensor nodes is an effective topology control approach that can balance the load on sensor nodes and increase network scalability and lifetime. But the environments of sensor networks are inherently dynamic and they are continuously in change (e.g. military environments). In addition the sensor nodes have limited energy supply and after consumption of it they will die, so it is necessary to replace these dead nodes with some fresh nodes occasionally. Thus the cluster structure of sensor networks has dynamic nature. At the Other hand the clustering process will impose some overhead to the network and it is not efficient to execute it more than once. Therefore an algorithm that can dynamically cluster sensor networks makes clustering more efficient. Our approach in this paper doesn't need to be executed periodically all over the network. Our proposed protocol DACA (dynamic advanced clustering algorithm) mainly would be executed at set up time all over the network and any time any changes are sensed in the network it locally reconfigures the cluster structure in the clusters that influenced by that changes and correct the cluster structure in a dynamic manner. This method reduces the overhead of clustering and prolongs the network lifetime. In addition the DACA can support multi-hop clusters. In multi-hop clusters the members of a cluster can be connected to their cluster-head in an indirect way with more than one hop. This is useful when communication cost in multi short hops is more efficient than one long hop (e.g. when the noise in the environment is high).
Computational Foresight: Forecasting Human Body Motion in Real-time for Reducing Delays in Interactive System

In this paper, we propose a machine learning-based system named "Computational Foresight" that can forecast human body motion 0.5 seconds before the actual motion in real-time. This forecasting system can be used to estimate human gestures in advance to the actual action for reducing delays in interactive system. In addition, the system can be applied to instruct sports actions properly, and prevent elderly from falling to the ground, and so on. Proposed system detects 25 human body joints to use those data for input dataset of machine learning. We created 5-layered neural network to estimate human body motion in real-time. In our experiment, we measured jump motions of subjects for learning. In our evaluation, the prototype system scored that the center of gravity of whole body can be forecasted 0.5 sec before with its accuracy of 7.9 cm.
Feature analysis and centromere segmentation of human chromosome images using an iterative fuzzy algorithm

Classification of homologous chromosomes is essential to advanced studies of cancer genetics. Centromere intensities are believed to be an important differentiating feature between homologs. Therefore, segmentation of centromeres is a major step toward the realization of homolog classification. This paper describes an iterative fuzzy algorithm which successfully segments centromeres from images of human chromosomes prepared using fluorescence in-situ hybridization technique. The algorithm is based on assigning a fuzzy membership value to each pixel in the centromere image. An iterative algorithm then updates and minimizes a defined error function. Chromosome 22, a highly heteromorphic chromosome, is used to verify the centromere segmentation method. Homologs of this chromosome are classified based on their segmented centromere intensities as well as their morphological differences. The classification results of these two methods agree completely and are used to validate our developed algorithm.
Designing & Teaching Learning Goals & Objectives

Design and teach effective learning goals and objectives by following strategies based on the strongest research and theories available. This first book in the Classroom Strategies That Work library includes a summary of key research behind these classroom practices and shows how to implement them using step-by-step hands-on strategies. Short quizzes help readers assess their understanding of the instructional best practices explained in each section. This self-study resource can be used by individuals, teams of teachers or an entire faculty to make improvements where it matters most: in classrooms.
INTERACTION BETWEEN STRESSES

Normally, under natural conditions, plants are subjected to the influence of at least two different stress factors. The physiological responses of plants, exposed to two subsequent stress factors differing in their intensity or duration are reviewed. In the experiments presented here, the effects of some natural (water depletion, extreme temperatures), and anthropogenic (UV-B irradiation and herbicides) stresses, applied alone and in combination were studied. As a measure of the interaction between stresses, the changes in biometric parameters, the levels of some oxidative stress markers and activity of defence enzymes were monitored in pea, wheat, or maize seedlings (grown as water culture) and in Arabidopsis plants. The relationships between the metabolic changes observed, and the degree of cross-synergism or crossadaptation to the interacting stresses are discussed.
The las campanas/AAT rich cluster survey - I. precision and reliability of the photometric catalogue

The Las Campanas Observatory and Anglo-Australian Telescope Rich Cluster Survey (LARCS) is a panoramic imaging and spectroscopic survey of an X-ray luminosity-selected sample of 21 clusters of galaxies at 0.07 < z < 0.16. Charge-coupled device (CCD) imaging was obtained in B and R of typically 2 degrees wide regions centred on the 21 clusters, and the galaxy sample selected from the imaging is being used for an on-going spectroscopic survey of the clusters with the 2dF spectrograph on the Anglo-Australian Telescope. This paper presents the reduction of the imaging data and the photometric analysis used in the survey. Based on an overlapping area of 12.3 deg(2) we compare the CCD-based LARCS catalogue with the photographic-based galaxy catalogue used for the input to the 2dF Galaxy Redshift Survey (2dFGRS) from the APM, to the completeness of the GRS/APM catalogue, b(J) = 19.45. This comparison confirms the reliability of the photometry across our mosaics and between the clusters in our survey. This comparison also provides useful information concerning the properties of the GRS/APM. The stellar contamination in the GRS/APM galaxy catalogue is confirmed as around 5-10 per cent, as originally estimated. However, using the superior sensitivity and spatial resolution in the LARCS survey evidence is found for four distinct populations of galaxies that are systematically omitted from the GRS/APM catalogue. The characteristics of the 'missing' galaxy populations are described, reasons for their absence examined and the impact they will have on the conclusions drawn from the 2dF Galaxy Redshift Survey are discussed.
Learning Generalized Weighted Relevance Aggregation Operators Using Levenberg-Marquardt Method

We previously introduced the generalized Weighted Relevance Aggregation Operators (WRAO) for hierarchical fuzzy signatures. WRAO enhances the ability of the fuzzy signature model to adapt to different applications and simplifies the learning of fuzzy signature models from data. In this paper we overcome the practical issues which occur when learning WRAO from data. This paper discuss an algorithm for learning WRAO using the Levenberg- Marquardt (LM) method, which is one of the most sophisticated and widely used gradient based optimization method. Also, this paper shows the successful results of applying the proposed algorithm to extract WRAO for two real world problems namely High Salary Selection and SARS Patient Classification.
Robust average current-mode control of multimodule parallel DC-DC PWM converter systems with improved dynamic response

This paper presents a novel average current-mode control (ACC) strategy for the control of multimodule parallel pulsewidth modulation DC-DC converters, which represents a drastic improvement over conventional ACC. This new method consists of the addition of an auxiliary controller into the control loop, besides the current and voltage regulators. The reference-model-based auxiliary controller improves the robustness of the ACC dynamics in buck-derived distributed power systems, preserving loop gain crossover frequency and stability margins over significant changes of the number of connected modules, the load and the line voltage. Moreover, this control scheme shows much better disturbance rejection properties, i.e., closed-loop output impedance and audiosusceptibility, than conventional ACC. From a control theory point of view robust performance is achieved, preserving stability. A multimodule buck prototype has been experimentally tested with different numbers of modules on stream, line, and load conditions, including discontinuous conduction mode. Measurements of the small-signal frequency response of the converter have been carried out, showing the improvement achieved by the proposed control scheme. The empirical large-signal response of the converter under load steps is also shown in order to validate the concept.
Emotion regulation and psychopathology : a transdiagnostic approach to etiology and treatment

Sloan, Kring, Introduction and Overview. Part 1. Models of Emotion Regulation: Insights from Basic Science. Werner, Gross, Emotion Regulation and Psychopathology: A Conceptual Framework. Thompson, Goodman, Development of Emotion Regulation: More Than Meets the Eye. Denny, Silvers, Ochsner, How We Heal What We Don't Want to Feel: The Functional Neural Architecture of Emotion Regulation. Bloch, Moran, Kring, On the Need for Conceptual and Definitional Clarity in Emotion Regulation Research on Psychopathology. Part 2. Problems of Emotion Regulation that Span Different Disorders: Descriptions, Mechanisms, Comorbidities. Boulanger, Hayes, Pistorello, Experiential Avoidance as a Functional Contextual Concept. Salters-Pedneault, Steenkamp, Litz, Suppression. Coifman, Bonanno, Emotion Context Sensitivity in Adaptation and Recovery. Joormann, Yoon, Siemer, Cognition and Emotion Regulation. Johnson, Carver, Fulford, Goal Dysregulation in the Affective Disorders. Dillon, Pizzagalli, Maximizing Positive Emotions: A Translational, Transdiagnostic Look at Positive Emotion Regulation. van der Helm, Walker, The Role of Sleep in Emotional Brain Regulation. Part 3. Treatment of Problems in Emotion Regulation. Fairholme, Boisseau, Ellard, Ehrenreich, Barlow, Emotions, Emotion Regulation, and Psychological Treatment: A Unified Perspective. Valdiva-Salas, Sheppard, Forsyth, Acceptance and Commitment Therapy in an Emotion Regulation Context. Corcoran, Farb, Anderson, Segal, Mindfulness and Emotion Regulation: Outcomes and Possible Mediating Mechanisms. Mennin, Fresco, Emotion Regulation as an Integrative Framework for Understanding and Treating Psychopathology. Taylor, Amir, Treatment of Problems in Emotion Regulation: Attention and Emotion Regulation. Syzdek, Addis, Martell, Working with Emotion and Emotion Regulation in Behavioral Activation Treatment for Depressed Mood. Harvey, McGlinchey, Gruber, Toward an Affective Science of Insomnia Treatments.
Automated classification of pathological gait after stroke using ubiquitous sensing technology

This study uses machine learning methods to distinguish between healthy and pathological gait. Examples of multi-dimensional pathological and normal gait sequences were collected from post-stroke and healthy individuals in a real clinical setting and with two Kinect sensors. The trajectories of rotational angle and global velocity of selected body joints (hips, spine, shoulders, neck, knees and ankles) over time formed the gait sequences. The combination of k nearest neighbor (kNN) and dynamic time warping (DTW) was used for classification. Leave one subject out cross validation was implemented to evaluate the performance of the binary classifier in terms of F1-score in the original feature space, and also in a reduced dimensional feature space using PCA. The pair of k = 1 in kNN and the warping window size 25% of gait sequences in DTW achieved maximum F1-score. Using PCA, pathological gait sequences were discriminated from healthy sequences with the F1-score = 96%.
Genetic contributions to cognitive ageing and structural brain magnetic resonance imaging phenotypes

As humans age, specific mental faculties deteriorate even in the absence of dementia. Age related cognitive decline affects quality of life, and has significant implications from a socio-economic perspective; however not everyone declines to equal degrees, at equal rates, or from the same baseline. This PhD examined a large sample of community-dwelling older adults called the Lothian Birth Cohort 1936, most of whom completed an intelligence test at age 11 years, and again around age 73 as part of a detailed assessment that also included detailed brain magnetic resonance imaging (N range = 700-866). I investigated the independent effects of two linked genetic loci which have been associated with greater risk of Alzheimer’s disease – the APOE e haplotype (commonly ‘genotype’) and a poly-T repeat in the TOMM40 gene. Are 'risk' variants in these loci associated with specific measures of cognitive ageing and brain structure - specifically white matter microstructural integrity, hippocampal volumes, white matter lesions or cerebral microbleeds – in this sample? Firstly, a pilot study aimed to replicate significant associations between the ADRB2 gene and brain imaging/cognitive phenotypes, that had previously been reported in a smaller subsample of the cohort that had by that time undergone MRI (n = 132). Previously reported significant associations were not significant in the larger, full LBC1936 sample (n = 700-866), but novel significant associations were found (P < 0.05). Specifically, integrity of the left arcuate fasciculus white matter tract significantly mediated part of the association between specific genetic variations at ADRB2, and the Digit Symbol Coding task of information processing speed. These findings indicated that this approach – testing three-way genetic/brain imaging/cognitive associations for mediation - was viable for the main APOE/TOMM40 analyses. Results in the main APOE/TOMM40 analyses showed that specific variants in the APOE and TOMM40 gene loci were statistically significantly associated (at raw P value <0.05) with white matter tract microstructural integrity, but not white matter lesions, hippocampal volume or cerebral microbleeds. Inconsistencies with previous, positive reports showing significant associations between APOE e and these latter phenotypes may reflect a degree of type 1 error or more study-specific discrepancies (which are detailed throughout). APOE e was significantly associated with average scores on a large proportion of cognitive tests, independent of age 11 intelligence (i.e. ‘cognitive ageing’; Deary et al., 2004). These associations were partly – but not completely – mediated by white matter tract microstructural integrity. TOMM40 poly-T repeat genotype was associated with cognitive ageing to a much lesser extent. A range of brain phenotypes may form the anatomical basis for significant associations between APOE genotype and cognitive ageing, among which includes white matter tract microstructural integrity.
The ethical use of touch in psychotherapy

PART ONE: FOUNDATIONS AND HISTORICAL BACKGROUND The Physiology of Touch The Influence of Touch on Socialization Traditions of Touch within Various Cultures Traditions of Touch in Psychotherapy PART TWO: CLINICAL APPLICATION OF TOUCH IN PSYCHOTHERAPY Power Dynamics that Affect the Use of Touch in Therapy Functions of Touch in Psychotherapy The Dynamics of Touch When Applied in Psychotherapy Guidelines for Using Touch in Psychotherapy A Continuum of Touch Specific Techniques and Case Examples Involving the Use of Touch The Use of Touch in Various Modalities and with Specific Populations The Use of Bodyworkers as an Adjunct to Psychotherapy PART THREE: MATERIALS AND TRAINING AIDS Erotic Issues within the Psychotherapy Relationship Examining Personal Views on the Use of Touch in Psychotherapy
Reconstructing biochemical pathways from time course data

Time series data on biochemical reactions reveal transient behavior, away from chemical equilibrium, and contain information on the dynamic interactions among reacting components. However, this information can be difficult to extract using conventional analysis techniques. We present a new method to infer biochemical pathway mechanisms from time course data using a global nonlinear modeling technique to identify the elementary reaction steps which constitute the pathway. The method involves the generation of a complete dictionary of polynomial basis functions based on the law of mass action. Using these basis functions, there are two approaches to model construction, namely the general to specific and the specific to general approach. We demonstrate that our new methodology reconstructs the chemical reaction steps and connectivity of the glycolytic pathway of Lactococcus lactis from time course experimental data.
Online feature selection for mining big data

Most studies of online learning require accessing all the attributes/features of training instances. Such a classical setting is not always appropriate for real-world applications when data instances are of high dimensionality or the access to it is expensive to acquire the full set of attributes/features. To address this limitation, we investigate the problem of Online Feature Selection (OFS) in which the online learner is only allowed to maintain a classifier involved a small and fixed number of features. The key challenge of Online Feature Selection is how to make accurate prediction using a small and fixed number of active features. This is in contrast to the classical setup of online learning where all the features are active and can be used for prediction. We address this challenge by studying sparsity regularization and truncation techniques. Specifically, we present an effective algorithm to solve the problem, give the theoretical analysis, and evaluate the empirical performance of the proposed algorithms for online feature selection on several public datasets. We also demonstrate the application of our online feature selection technique to tackle real-world problems of big data mining, which is significantly more scalable than some well-known batch feature selection algorithms. The encouraging results of our experiments validate the efficacy and efficiency of the proposed techniques for large-scale applications.
Radioimmunotherapy of human colon carcinoma by 131I‐labelled monoclonal anti‐cea antibodies in a nude mouse model

A mixture of 3 MAbs directed against 3 different CEA epitopes was radiolabelled with 131I and used for the treatment of a human colon carcinoma transplanted s.c. into nude mice. Intact MAbs and F(ab′)2 fragments were mixed because it had been shown by auto radiography that these 2 antibody forms can penetrate into different areas of the tumor nodule. Ten days after transplantation of colon tumor T380 a single dose of 600 μCi of 131I MAbs was injected i.v. The tumor grafts were well established (as evidenced by exponential growth in untreated mice) and their size continued to increase up to 6 days after radiofabelled antibody injection. Tumor shrinking was then observed lasting for 4–12 weeks. In a control group injected with 600 μCJ of 131I coupled to irrelevant monoclonal IgG, tumor growth was delayed, but no regression was observed. Tumors of mice injected with the corresponding amount of unlabelled antibodies grew like those of untreated mice. Bated on measurements of the effective whole‐body half‐life of injected 131I, the mean radiation dose received by the animals was calculated to be 382 rads for the antibody group and 478 rads for the normal IgG controls. The genetically immunodeficient animals exhibited no increase in mortality, and only limited bone‐marrow toxicity was observed. Direct measurement of radioactivity in mice dissected 1, 3 and 7 days after 131I‐MAb injection showed that 35, 7.2 and 7–2% of injected dose were recovered per gram of tumor, the mean radiation dose delivered to the tumor being thus more than 5,000 rads. These experiments show that therapeutic doses of radioactivity can be selectively directed to human colon carcinoma by i.v. injection of 131I‐labelled anti‐CEA MAbs.
Soft Cross-lingual Syntax Projection for Dependency Parsing

This paper proposes a simple yet effective framework of soft cross-lingual syntax projection to transfer syntactic structures from source language to target language using monolingual treebanks and large-scale bilingual parallel text. Here, soft means that we only project reliable dependencies to compose high-quality target structures. The projected instances are then used as additional training data to improve the performance of supervised parsers. The major issues for this idea are 1) errors from the source-language parser and unsupervised word aligner; 2) intrinsic syntactic non-isomorphism between languages; 3) incomplete parse trees after projection. To handle the first two issues, we propose to use a probabilistic dependency parser trained on the target-language treebank, and prune out unlikely projected dependencies that have low marginal probabilities. To make use of the incomplete projected syntactic structures, we adopt a new learning technique based on ambiguous labelings. For a word that has no head words after projection, we enrich the projected structure with all other words as its candidate heads as long as the newly-added dependency does not cross any projected dependencies. In this way, the syntactic structure of a sentence becomes a parse forest (ambiguous labels) instead of a single parse tree. During training, the objective is to maximize the mixed likelihood of manually labeled instances and projected instances with ambiguous labelings. Experimental results on benchmark data show that our method significantly outperforms a strong baseline supervised parser and previous syntax projection methods.
Peatlands as Filters for Polluted Mine Water?—A Case Study from an Uranium-Contaminated Karst System in South Africa Part II: Examples from Literature and a Conceptual Filter Model

As the second part of a series of four, this paper reviews a number of case studies of natural uranium attenuation in peat, as well as underlying chemical mechanisms reported in literature. Based on this review, a generic, conceptual, model for peat to act as filter for dissolved uranium (U) is developed for guiding subsequent field investigations. The model consists of a chemical and an hydraulic component which is derived largely from data reported in literature as well as from limited field observations. For the chemical model component 10 different processes, each controlled by factors relating to water chemistry, have been identified to govern the attenuation of U in peat via a net balance of immobilization and remobilization. For the hydraulic aspect of the filter model, five different principal modes of U polluted water coming in contact with peat are discussed, focusing on the associated peat-water contact time as a crucial parameter controlling chemical U attenuation. Moreover, links between the two model components are discussed and, based on the integrated conceptual model, possible effects of natural and anthropogenic events on U attenuation in peatlands are outlined. Guided by the model, various site-specific field and laboratory investigations are finally designed to verify how far the identified generic factors and processes are indeed applicable to the Gerhard Minnebron Peatland.