Miracle-dz/UniFAR-FTUs huggingface dataset:
A new single phase single stage power factor corrected converter

The paper deals with a new single stage three level ac-dc converter which performs both power factor correction and voltage regulation in a single stage. The proposed converter has two separate controllers, one for power factor correction and the other for regulating the output voltage. A comprehensive review of the existing single stage topologies has been carried out. Then the operating principle, control scheme and the design of the new converter are presented. The proposed converter is having an input power factor close to unity and better voltage regulation compared to the conventional ac-dc converter topologies. Simulation results obtained from a 1.35kW model are also presented to show the feasibility of the new converter.
Discovering Condition-Dependent Bayesian Networks for Gene Regulation

Among the main interests in many biological studies are the structure of gene regulatory network, and in particular differences in the regulatory interactions between different conditions. However, since the number of available samples is always very small and estimating the network structure is extremely hard, most current algorithms have to assume that the gene regulation does not change between conditions. We propose a new Bayesian network algorithm which (i) utilizes all the samples for estimating regulatory relations that remain the same across conditions, and (ii) explicitly searches for regulatory relationships that are active only in one of the conditions. The result is an easily interpretable map of changes in regulation in several conditions.
Optimal reactive power control for industrial power networks

Manufacturing processes that have several main buses that supply power to other areas of the plant have to ensure the voltages on the buses remain close to the desired value. If the voltage should drop there could be loss of process functions due to under voltage tripping of motors, pumps etc. In addition, the utility often imposes a penalty for low tie line power factor. By controlling the bus voltages close to desired values while keeping tie-line power factor close to unity, reliability of plant operation is increased while minimizing costs. The authors describe a reactive power optimisation method based on the Simplex Self Directing Evolutionary Operation (SSDEVOP) method proposed by Box (1957). The construction of a static load flow analysis (SLFA) admittance matrix and network configuration adjustments are discussed. The implementation of the system, off-line SLFA program overview, the distributed control system environment and three case studies are also discussed.
Intermittent Fault Diagnosis in Wireless Sensor Networks

This paper proposes a probabilistic approach to diagnose intermittent faults in a wireless sensor network (WSN). Though system level diagnosis has become an important technique to diagnose faults in wired networks, its applicability to wireless network has not been well studied. Though, intermittent faults are most probable in WSN, diagnosis of such faults has not been well explored. The algorithm proposed in this paper has been evaluated through analytical means and is simulated assuming a 512 sensor nodes interconnected to form an arbitrary network topology. The results show that accurate diagnosis of intermittent faults in WSN is feasible.
Toxicity Evaluation of Graphene and Poly(Lactic-Acid) Using a Nematode Model

Graphene has gained tremendous attention due to its unlimited potential in various applications while poly(lactic acid) (PLA) is a biodegradable thermoplastic polyester produced from fermenting corn starch. The incorporation of graphene into PLA has been proven to exhibit excellent mechanical and thermal properties. However, there are not many reports on the potential toxic effect of these materials towards living organisms. In this study, we investigated the possible toxicity of graphene and PLA-graphene in a live animal model, the nematode Caenorhabdits elegans (C. elegans). Alive adult worms were exposed directly to graphene and PLA-graphene across a range of concentrations from 50 µg/mL to 1000 µg/mL. After certain hours of exposure, the pharyngeal pumping rate (indicative of the C. elegans feeding activity), reproductive rate and lifespan of the worms were determined and compared to the untreated worm population. At all concentrations tested, both graphene and PLA-graphene do not affect the feeding rate of the nematode. Additionally, there was no significant difference between the lifespan of worms exposed to graphene and PLA-graphene as compared to the untreated control population (p>0.05). We examined the effect of graphene on nematode’s ability to reproduce and no reduction in progenies was detected (p>0.05). Taken together, our findings suggest that graphene and PLA-graphene do not possess a negative effect on the feeding activity, reproduction and overall lifespan of the host, indicating that these materials are safe to living organism at concentration up to 1000 µg/mL.
A Large Scale Corpus of Gulf Arabic

Most Arabic natural language processing tools and resources are developed to serve Modern Standard Arabic (MSA), which is the official written language in the Arab World. Some Dialectal Arabic varieties, notably Egyptian Arabic, have received some attention lately and have a growing collection of resources that include annotated corpora and morphological analyzers and taggers. Gulf Arabic, however, lags behind in that respect. In this paper, we present the Gumar Corpus, a large-scale corpus of Gulf Arabic consisting of 110 million words from 1,200 forum novels. We annotate the corpus for sub-dialect information at the document level. We also present results of a preliminary study in the morphological annotation of Gulf Arabic which includes developing guidelines for a conventional orthography. The text of the corpus is publicly browsable through a web interface we developed for it.
Allograft Versus Autograft Anterior Cruciate Ligament Reconstruction

Background: Tearing an anterior cruciate ligament (ACL) graft is a devastating occurrence after ACL reconstruction (ACLR). Identifying and understanding the independent predictors of ACLR graft failure is important for surgical planning, patient counseling, and efforts to decrease the risk of graft failure. Hypothesis: Patient and surgical variables will predict graft failure after ACLR. Study Design: Prospective cohort study. Methods: A multicenter group initiated a cohort study in 2002 to identify predictors of ACLR outcomes, including graft failure. First, to control for confounders, a single surgeon’s data (n = 281 ACLRs) were used to develop a multivariable regression model for ACLR graft failure. Evaluated variables were graft type (autograft vs allograft), sex, age, body mass index, activity at index injury, presence of a meniscus tear, and primary versus revision reconstruction. Second, the model was validated with the rest of the multicenter study’s data (n = 645 ACLRs) to evaluate the generalizability of the model. Results: Patient age and ACL graft type were significant predictors of graft failure for all study surgeons. Patients in the age group of 10 to 19 years had the highest percentage of graft failures. The odds of graft rupture with an allograft reconstruction are 4 times higher than those of autograft reconstructions. For each 10-year decrease in age, the odds of graft rupture increase 2.3 times. Conclusion: There is an increased risk of ACL graft rupture in patients who have undergone allograft reconstruction. Younger patients also have an increased risk of ACL graft failure. Clinical Relevance: Given these risks for ACL graft rupture, allograft ACLRs should be performed with caution in the younger patient population.
Aggregating Similarity Measures based Ontology on Documents Retrieval

This paper investigates a methodology for the onto l gy based semantic retrieval of annotated web documents with terms occurrence weigh ting. The semantic structural distance of document terms in terms of domain ontol gy is computed against new unknown queries to improve the documents ranking an d retrieval. Furthermore, the role of aggregation methods to combine the weighting ter ms scheme based similarity measures with the similarity of semantic distance w ith respect to ontology have been carried out. Domain ontology is developed in which it defines a number of keywords and properties for semantic reasoning. The performance of the proposed method has been evaluated on health documents from a number of aspe cts, and experimental results are encouraging showing its effectiveness.
Loss Calibarated Methods for Bipartite Rationing Problems

The standard problem of rationing a single overdemanded commodity (O’Neill [1982], Aumann and Maschler [1985], Young [1987]) has a natural bipartite extension with multiple types of a single commodity (e.g., jobs with different skill requirements), and each agent can only consume some types of the resource (e.g., workers have different skills). We define the new standard loss calibrated rationing methods, that equalize across agents the ratio of shares to (calibrated) losses (demand minus share). We extend them to bipartite methods that 1) are not affected by the elimination of an edge and the corresponding flow (Consistency), and 2) treat resource types with identical connectivity as a single type. They are essentially the only standard methods with a bipartite extension meeting the two properties above. Most of the parametric methods discussed in
Performance optimization using separator sets

In this paper, we propose a new method to optimize a performance of a very large circuit. We find the best set of local transformations to be applied to the circuit, by inserting "padding nodes" on noncritical edges of the circuit, and calculating separator sets of the circuit using separator sets. Our method is robust for very large circuits, because its memory usage and calculation time are linear and polynomial order with the size of the circuit. According to our experimental results, our method has accomplished all circuits, while Singh's (1992) selection function method has aborted with three large circuits because of memory overflow. The results also shows our method has a comparable capability in delay optimization to Singh's method.
Fractal approach to low-rate video coding

This paper presents a method for fast encoding and decoding of image sequences based on fractal coding theory and the hybrid coding concept. The DPCM-loop accounts for statistical dependencies of natural image sequences in temporal direction. Those regions of the original image where the prediction, i.e., motion estimation, and compensation fails are encoded using an advanced fractal coding scheme which originally was developed for encoding of still images. Similar to conditional replenishment coders, not regions of the residual image itself but of the original image are encoded. The introduction of a fractal coding scheme instead of the commonly used DCT turns out to be advantageous especially at very low bit rates (8 - 64 kbit/s). In order to increase reconstruction quality, encoding speed and compression ratio, some additional features such as hierarchical codebook search and multilevel block segmentation, are proposed.
The German Journal of Psychology a Quarterly Reviews, Reports and Comprehensive Abstracts Managing Editors S*——

Experimental and P h y s i o l o g i c a l Psychology No. Page 115 Split-brain and laterality: A comparison of manual Performance in intact and callosotomized rhesus monkeys (Macaca mulatta). By H . E . Engele . 301 116 Stress reactions in a competitive working Situation: The effects of different Performance levels of competitors. By W. Krieger and P. Schulz 303 117 Speech chronemics: A hidden dimension of speech theoretical background, measurement and clinical validity. By H. -P. Krüger 304 118 Processing structures for perception and action: Final report of the Sonderforschungsbereich "Kybernetik" 1969-1983. By H . Marko, G. Hauske and A . Struppler 306 119 Knowledge acquisition and improvement in System control. By W. P u t z O s t e r l o h , B . B o t t and /. H o u b e n 308 120 Visible speech: Examinations of different modalities. By M. Ruoß, R.Becker and AT. Eyferth 309
Detecting self-stimulatory behaviours for autism diagnosis

Autism Spectrum Disorders (ASD), often referred to as autism, are neurological disorders characterised by deficits in cognitive skills, social and communicative behaviours. A common way of diagnosing ASD is by studying behavioural cues expressed by the children. An algorithm for detecting three types of self-stimulatory behaviours from publicly available unconstrained videos is proposed here. The child's body is tracked in the video by a careful selection of poselet bounding box predictions using a nearest neighbour algorithm. A global motion descriptor - Histogram of Dominant Motions (HDM) - is computed using the dominant motion flow in the detected body regions. The motion model built using this descriptor is used for detecting the self-stimulatory behaviours. Experiments conducted on the recently released unconstrained SSBD video dataset show significant improvement in detection accuracy over the baseline approach. The robustness of the method is validated using benchmark action recognition datasets. The proposed poselet bounding box selection algorithm is validated against the ground truth annotation data provided with the UCF101 dataset.
The Methodology and Research Participation Experiences of Participants in the Aborted Suicide Attempt Study

The participants’ study participation experiences in an intensive research methodology that examined recent aborted suicide attempts were explored. Individuals aged 18 or above and whose suicide attempts were voluntarily aborted after the suicide crisis negotiation with the police negotiators were interviewed and followed up at 1 and 3 months after the initial interview. Eleven participants gave their consent to the Police Negotiation Cadre of the Hong Kong Police Force for further contact by the research team during June 2012 and December 2014. Nine aborted attempters completed the semi-structured interviews conducted by the research team about their suicide attempt processes and the events leading to that. Seven of them completed the 3 months follow up. The majority of the participants were positive about being approached to take part in the study. They found the experience of talking about their suicide attempts more positive than expected, and felt the study participation was useful for them and meaningful for the society. None of the participants reported being distressed by the recruitment method or the interviews. Results suggest that such sensitive non-treatment research when conducted ethically, participants could benefit from it.
Towards a Resource Elasticity Benchmark for Cloud Environments

Auto-scaling features offered by today's cloud infrastructures provide increased flexibility especially for customers that experience high variations in the load intensity over time. However, auto-scaling features introduce new system quality attributes when considering their accuracy, timing, and boundaries. Therefore, distinguishing between different offerings has become a complex task, as it is not yet supported by reliable metrics and measurement approaches. In this paper, we discuss shortcomings of existing approaches for measuring and evaluating elastic behavior and propose a novel benchmark methodology specifically designed for evaluating the elasticity aspects of modern cloud platforms. The benchmark is based on open workloads with realistic load variation profiles that are calibrated to induce identical resource demand variations independent of the underlying hardware performance. Furthermore, we propose new metrics that capture the accuracy of resource allocations and de-allocations, as well as the timing aspects of an auto-scaling mechanism explicitly.
Agents with Faces: The Effects of Personification of Agents

It is still an open question whether software agents should be personified in the interface. How should faces be used to make the agent more usable and likable? In order to study the effect of faces and facial expressions, we compared subjects' responses to and evaluation of different faces and facial expressions of entertaining agents. The result shows that having faces and facial expressions is likable and engaging. It takes users' effort to interpret the meaning the faces. In terms of facial features, realistic faces are better liked and rated as more intelligent than abstract faces.
Forward Decay: A Practical Time Decay Model for Streaming Systems

Temporal data analysis in data warehouses and datastreaming systems often uses time decay to reduce the importance of older tuples, without eliminating their influence, on the results of the analysis. While exponential time decay is commonly used in practice, other decay functions (e.g. polynomial decay) are not, even though they have been identified as useful. We argue that this is because the usual definitions of time decay are "backwards": the decayed weight of a tuple is based on its age, measured backward from the current time. Since this age is constantly changing, such decay is too complex and unwieldy for scalable implementation. In this paper, we propose a new class of "forward" decay functions based on measuring forward from a fixed point in time. We show that this model captures the more practical models already known, such as exponential decay and landmark windows, but also includes a wide class of other types of time decay. We provide efficient algorithms to compute a variety of aggregates and draw samples under forward decay, and show that these are easy to implement scalably. Further, we provide empirical evidence that these can be executed in a production data stream management system with little or no overhead compared to the undecayed computations. Our implementation required no extensions to the query language or the DSMS, demonstrating that forward decay represents a practical model of time decay for systems that deal with time-based data.
Detection and localization of abnormal activities in video surveillance system

Problem of detection and localization of abnormal behaviors in crowded scenes is considered in this paper. To extract various activities of crowd, spatiotemporal Laplacian eigenmap method is proposed. Here, spatiotemporal variations of local motions are learned in an embedded space. Regular behavior of a crowd is modeled using representatives of various activities. To detect abnormal behavior in local as well as global contexts and to localize regions showing abnormality, model of regular crowd behavior is used. Computational simplicity is the feature of this method.
Modalités minimales d'interactions pour la pratique du débat en ligne

Nous nous interessons a la problematique des services de debat en ligne dans des contextes de democratie participative electronique. De tels services sont des enjeux politiques majeurs, ce sont des outils permettant de replacer les citoyens au centre du debat politique et de court-circuiter une partie de l'inertie liee a la delegation. Un des problemes majeurs de tels outils est le cout de traitement des contenus produits lors de debats a grande echelle. Pour lever ce verrou nous proposons l'utilisation de modalites discursives et experimentons leur praticabilite et utilite percue.
Learning of Uncontrolled Restless Bandits with Logarithmic Strong Regret

In this paper we consider the problem of learning the optimal dynamic policy for uncontrolled restless bandit problems. In an uncontrolled restless bandit problem, there is a finite set of arms, each of which when played yields a non-negative reward. There is a player who sequentially selects one of the arms at each time step. The goal of the player is to maximize its undiscounted reward over a time horizon T . The reward process of each arm is a finite state Markov chain, whose transition probabilities are unknown to the player. State transitions of each arm is independent of the player’s actions, thus “uncontrolled”. We propose a learning algorithm with near-logarithmic regret uniformly over time with respect to the optimal (dynamic) finite horizon policy, referred to as strong regret, to contrast with commonly studied notion of weak regret which is with respect to the optimal (static) single-action policy. We also show that when an upper bound on a function of the system parameters is known, our learning algorithm achieves logarithmic regret. Our results extend the literature on optimal adaptive learning of Markov Decision Processes (MDPs) to Partially Observed Markov Decision Processes (POMDPs). Finally, we provide numerical results on a variation of our proposed learning algorithm and compare its performance and running time with other bandit algorithms.
Location Similarity Based Replica Node Detection for Sensor Networks

The node replica attack is known to be dangerous to wireless sensor networks (WSNs) because it enables the adversary to extend the damage throughout the network with very low cost. To stop such attack, we propose a similarity estimation based scheme with group deployment knowledge. Compared with prior works, our proposal provides extra functionality that prevent replica from generating false location claims. Through simulation experiment, we evaluate the performance of the proposed scheme and demonstrate that our scheme achieves effectiveness and efficiency under different situations and attack strategies.
Socioeconomic disparities in children’s cognitive development: Longitudinal dynamics and stress mechanisms

Vorsitz: Prof. Dr. Denis Gerstorf, Department of Psychology, Humboldt–Universität zu Berlin Gutachter: 1. Prof. Dr. Ulman Lindenberger, Center for Lifespan Psychology, Max Planck Institute for Human Development; Department of Psychology, Humboldt–Universität zu Berlin 2. Prof. Dr. Christine Heim, Institute for Medical Psychology, Charité – Universitätsmedizin Berlin; Department of Biobehavioral Health, Pennsylvania State University 3. Prof. Dr. Kimberly Noble, Teachers College, Columbia University Betreuer: Prof. Dr. Yee Lee Shing, Prof. Dr. Christine Heim, Prof. Dr. Ulman Lindenberger
Clustering with qualitative information

We consider the problem of clustering a collection of elements based on pairwise judgments of similarity and dissimilarity. N. Bansal et al. (2002) cast the problem thus: given a graph G whose edges are labeled "+" (similar) or "-" (dissimilar), partition the vertices into clusters so that the number of pairs correctly (resp. incorrectly) classified with respect to the input labeling is maximized (resp. minimized). Complete graphs, where the classifier labels every edge, and general graphs, where some edges are not labeled, are both worth studying. We answer several questions left open by N. Bansal et al. (2002) and provide a sound overview of clustering with qualitative information. We give a factor 4 approximation for minimization on complete graphs, and a factor O(log n) approximation for general graphs. For the maximization version, a PTAS for complete graphs is shown by N. Bansal et al. (2002); we give a factor 0.7664 approximation for general graphs, noting that a PTAS is unlikely by proving APX-hardness. We also prove the APX-hardness of minimization on complete graphs.