Miracle-dz/UniFAR-FTUs huggingface dataset:
Leadership for learning in the preschool: Preschool managers’ perspectives on strategies and actions in the systematic quality work

This article presents the results of a qualitative study with the aim of contributing to an understanding of preschool managers’ leadership in systematic quality work in a Swedish context. In line with the Education Act and the preschool curriculum, systematic quality work is compulsory in all Swedish preschools. This consists of several steps, including data collection, documentation and self-evaluation for the purpose of professional development and quality improvement. With the aid of interviews, preschool managers’ perspectives on leadership actions, with an emphasis on a leadership for learning, have been analysed in relation to local conditions and previous research. The results show that preschool managers balance the demands for loyalty to the municipality’s quality system’s requirements with their own preschool’s independence in the systematic quality work. In different ways they emphasise the organisational, content-related and methodological aspects of their long-term strategies, but also use relational and organisational leadership actions and feedback to show how they expect the staff to carry out their work with the children and collaborate with colleagues. The preschool’s conditions, such as the staff’s education levels, affect how the systematic quality work is organised and led. The importance of preschool managers’ professional knowledge – a content-related dimension – is discussed in relation to previous research on the concept of leadership for learning. One of the study’s conclusions is that the preschool’s leadership emphasis on organisational solutions could impede the preschool’s quality development.
Backward compatible enhancement of chroma format in HEVC

First version of the latest video coding standard, High Efficiency Video Coding (HEVC), only supports coding of video in YUV 4:2:0 chroma format. An extension of the standard that will support other chroma formats is currently under development, however, version 1 decoders will not be able to handle the bitstreams created using this extension. In this paper, we propose a novel method to create scalable bitstreams that involve a backward compatible base layer in 4:2:0 format that can be handled by HEVC version 1 decoders and code additional layers to enhance the chroma resolution. The proposal codes 4:2:0 video in the base layer and the high resolution chroma components as auxiliary pictures as separate enhancement layers. The high resolution chroma components could optionally be predicted from the upsampled 4:2:0 chroma components of base layer. The simulations show that the proposed method achieves scalability with 9.5% coding efficiency penalty on average compared to single layer coding of 4:4:4 video. When compared to simulcast of 4:2:0 and 4:4:4 video, proposed method provides 38% gain on average. Proposed method makes services using high chroma fidelity easier to be deployed, due to the backwards compatibility to existing HEVC implementations with high coding efficiency.
The Minimum Scheduling Time for Convergecast in Wireless Sensor Networks

We study the scheduling problem for data collection from sensor nodes to the sink node in wireless sensor networks, also referred to as the convergecast problem. The convergecast problem in general network topology has been proven to be NP-hard. In this paper, we propose our heuristic algorithm (finding the minimum scheduling time for convergecast (FMSTC)) for general network topology and evaluate the performance by simulation. The results of the simulation showed that the number of time slots to reach the sink node decreased with an increase in the power. We compared the performance of the proposed algorithm to the optimal time slots in a linear network topology. The proposed algorithm for convergecast in a general network topology has 2.27 times more time slots than that of a linear network topology. To the best of our knowledge, the proposed method is the first attempt to apply the optimal algorithm in a linear network topology to a general network topology.
Multi-class Semantic Video Segmentation with Exemplar-Based Object Reasoning

We tackle the problem of semantic segmentation of dynamic scene in video sequences. We propose to incorporate foreground object information into pixel labeling by jointly reasoning semantic labels of super-voxels, object instance tracks and geometric relations between objects. We take an exemplar approach to object modeling by using a small set of object annotations and exploring the temporal consistency of object motion. After generating a set of moving object hypotheses, we design a CRF framework that jointly models the super voxel and object instances. The optimal semantic labeling is inferred by the MAP estimation of the model, which is solved by a single move-making based optimization procedure. We demonstrate the effectiveness of our method on three public datasets and show that our model can achieve superior or comparable results than the state of-the-art with less object-level supervision.
Convective contributions to the frequencies of solar oscillations

Differences between observed and theoretical eigen- frequencies of the Sun have characteristics which identify them as arising predominantly from properties of the oscillations in the vicinity of the solar surface: in the super-adiabatic, con- vective boundary layer and above. These frequency differences may therefore provide useful information about the structure of these regions, precisely where the theory of solar structure is most uncertain. In the present work we use numerical simulations of the outer part of the Sun to quantify the influence of turbulent con- vection on solar oscillation frequencies. Separating the influ- ence into effects on the mean model and effects on the physics of the modes, we find that the main model effects are due to the turbulent pressure that provides additional support against grav- ity, and thermal differences between average 3-D models and 1-D models. Surfaces of constant pressure in the visible pho- tosphere are elevated by about 150 km, relative to a standard envelope model. As a result, the turning points of high-frequency modes are raised, while those of the low-frequency modes remain essen- tially unaffected. The corresponding gradual lowering of the mode frequencies accounts for most of the frequency difference between observations and standard solar models. Additional ef- fects are expected to come primarily from changes in the physics of the modes, in particular from the modulation of the turbulent pressure by the oscillations.
Feature Level Clustering of Large Biometric Database

This paper proposes an efficient technique for partitioning large biometric database during identification. In this technique feature vector which comprises of global and local descriptors extracted from offline signature are used by fuzzy clustering technique to partition the database. As biometric features posses no natural order of sorting, thus it is difficult to index them alphabetically or numerically. Hence, some supervised criteria is required to partition the search space. At the time of identification the fuzziness criterion is introduced to find the nearest clusters for declaring the identity of query sample. The system is tested using bin-miss rate and performs better in comparison to traditional k-means approach.
An Unexpected Innocent Complication Associated with Azacitidine Treatment of Myelodysplastic Syndrome: Erythema Annulare Centrifugum

Skin lesions accompanying hematological malignancies can be formed due to either direct tumor infiltration of the skin or indirect effects. Indirectly developing lesions may be a component of paraneoplastic syndrome. Erythema annulare centrifugum (EAC) is considered to be a hypersensitivity reaction developed against various antigens associated with infections, drugs, and endocrine diseases. EAC, rarely seen in neoplastic diseases, has been reported in lymphoma, leukemia, histiocytosis, and prostate cancer. Here we report EAC in a patient using a hypomethylating agent, azacitidine.
Weighted sum rate maximization in the MIMO Interference Channel

Centralized algorithms for weighted sum rate (WSR) maximization for the K-user frequency-flat MIMO Interference Channel (MIMO IFC) with full channel state information (CSI) are considered. Maximization of WSR is desirable since it allows the system to cover all the rate tuples on the rate region boundary for a given MIMO IFC. First, we propose an iterative algorithm to design optimal linear transmitters and receivers. The transmitters and receivers are optimized to maximize the WSR of the MIMO IFC. Subsequently, we propose a greedy user selection algorithm based on the maximum WSR algorithm that can be applied to select a subset of transmit-receive pairs that cooperate in the interest of maximizing the sum-rate of the resulting cooperative network. To the best of our knowledge this is the first time user selection has been proposed in the context of the MIMO IFC.
A Novel Algorithm For the Analysis Of Array Cgh Data

DNA copy number aberrations are common in cancer and other diseases. Newly developed array CGH technologies enable simultaneous measurement of DNA copy numbers for tens of thousands of sites within a genome. In array CGH experiments, DNA copy number of a test DNA sample relative to the DNA copy number of a reference DNA sample is measured. These relative measurements are mapped to their corresponding chromosomal locations. DNA copy gains and losses are then detected as deviations from the normal reference at specific chromosomal locations. In this paper, we introduce a novel algorithm to automatically identify the regions of DNA copy number gain and loss from array CGH data through a multi-scale edge detection algorithm. We demonstrate the method on two array CGH datasets. Our results show that this method can be successfully applied for the analysis of array CGH biological data
Building ontologies from folksonomies and linked data: Data structures and Algorithms

We present the data structures and algorithms used in the approach for building domain ontologies from folksonomies and linked data. In this approach we extracts domain terms from folksonomies and enrich them with semantic information from the Linked Open Data cloud. As a result, we obtain a domain ontology that combines the emergent knowledge of social tagging systems with formal knowledge from Ontologies.
Initial Knowledge Representation Formalism

for dissemination) This document describes a recommended knowledge representation formalism that meets the requirements of LarKC. More specifically it provides an analysis of requirements stemming from the use cases in the project and surveys the landscape of existing knowledge representation languages on the Semantic Web. Furthermore it examines systems that are able to perform inference at a large scale. Based on these results it proceeds to define its two main contributions: (i) A framework that allows for the specification of languages within the established Semantic Web language stack by providing the used vocabulary along with a set of entailment rules, and (ii) a recommended language that goes slightly beyond RDFS and allows two slightly different semantics according to the specific requirements in a scenario. This language was determined based on both theoretical complexity results and practicability of its implementation i.e. in existing inference systems. It is balanced around two goals: To guarantee a baseline of interoperability between different scenarios by means of a common language and by being open ended and lightweight enough to allow for use-case specific extensions within the defined framework, and by this means to support the open nature of the LarKC platform. As such it is a tractable minimal language that supports the overall vision of LarKC.
Network supported congestion avoidance in software-defined networks

Large IP networks tend to have a highly heterogeneous structure with many different transmission segments, each endowed with individual characteristics like data rate or delay. Obviously, applications find it difficult to precisely adjust their sending behavior to the underlying topology. Basically, a higher network utilization without congestion is desirable. Server applications demand for reliable information about the available data rate in the network in order to provide a sufficient quality to the clients. Traditionally, this information is completely gathered by source and sink without interaction with the network infrastructure. The rise of software-defined networks (SDNs) enables the integration of the network into this process. In this paper we propose a rate control mechanism for software-defined networks to avoid congestion situations. The approach enables clients to communicate with the network to bidirectionally exchange information about available and occupied data rate. We found that this customized communication approach enables better network load distribution and application transmission reaction - which can be easily implemented with SDNs. Ultimately, the controller finds an efficient forwarding path in the network and the clients have a reliable upper bound for their sending data rate.
Hierarchical modeling of temporal course in emotional expression for speech emotion recognition

This paper presents an approach to hierarchical modeling of temporal course in emotional expression for speech emotion recognition. In the proposed approach, a segmentation algorithm is employed to hierarchically chunk an input utterance into three-level temporal units, including low-level descriptors (LLDs)-based sub-utterance level, emotion profile (EP)-based sub-utterance level and utterance level. An emotion-oriented hierarchical structure is constructed based on the three-level units to describe the temporal emotion expression in an utterance. A hierarchical correlation model is also proposed to fuse the three-level outputs from the corresponding emotion recognizers and further model the correlation among them to determine the emotional state of the utterance. The EMO-DB corpus was used to evaluate the performance on speech emotion recognition. Experimental results show that the proposed method considering the temporal course in emotional expression provides the potential to improve the speech emotion recognition performance.
Robust wheelchair pedestrian detection using sparse representation

Detecting pedestrians with disability in surveillance videos is practical for the implementation of automated alert/assistance technology. This paper presents a novel approach for the dimensionality reduction which employs sparse representation to improve the generalization capability of a classifier. To characterize pedestrian with disability, we create directional maps by determining the dominant direction of motion in each local spatiotemporal region using 3D orientation filters, and then uses the maps in real-time surveillance settings to detect pre-defined types. Mathematically, the derived algorithm regards the input features as the dictionary in sparse representation, and selects the features that minimize the residual output error iteratively, thus the resulting features have a direct correspondence to the performance requirements of the given problem. Furthermore, the proposed algorithm can be regarded as a sparse classifier, which selects discriminative features and classifies the training data simultaneously. Experimental results obtained using the extensive dataset show the superior performance of our method and thus demonstrate its robustness with the novel sparse representation-based disabled pedestrian detector.
Trajectory Generation and Object Tracking of Mobile Robot Using Multiple Image Fusion

Detection of moving objects has been utilized in industrial robotic systems, for example, in the recognition and monitoring of unmanned systems that also require compression of moving images [1], [2], [3], [4]. Trajectory prediction of moving objects is required for a mobile manipulator that aims at the control and observation of motion information such as object position, velocity, and acceleration. Prediction and estimation algorithms have generally been required for industrial robots. For a simple example, in a pick-and-place operation with a manipulator, the precise motion estimation of the object on the conveyor belt is a critical factor in stable grasping. A well-structured environment, such as the moving-jig that carries the object on the conveyor belt and stops when the manipulator grasps the object, might obviate the motion estimation requirement. However, a wellstructured environment limits the flexibility of the production system, requires skillful designers for the jig, and incurs a high maintenance expense; eventually it will disappear from automated production lines. To overcome these problems, to tracking a moving object stably without stopping the motion, the trajectory prediction of the moving object on the conveyor belt is necessary [5]. The manipulator control system needs to estimate the most accurate position, velocity, and acceleration at any instance to capture the moving object safely without collision and to pick up the object stably without slippage. When the motion trajectory is not highly random and continuous, it can be modeled analytically to predict the near-future values based on previously measured data [6]. However, this kind of approach requires significant computational time for high-degrees-of-freedom motion, and its computational complexity increases rapidly when there are many modeling errors. In addition, performance is highly sensitive to the change of the environment. Those state-ofthe-art techniques perform efficiently to trace the movement of one or two moving objects but the operational efficiency decreases dramatically when tracking the movement of many moving objects because systems implementing multiple hypotheses and multiple targets suffer from a combinatorial explosion, rendering those approaches computationally very expensive for real-time object tracking [7]. O pe n A cc es s D at ab as e w w w . in te ch w eb . o rg
Age-related vascular stiffening: causes and consequences

Arterial stiffening occurs with age and is closely associated with the progression of cardiovascular disease. Stiffening is most often studied at the level of the whole vessel because increased stiffness of the large arteries can impose increased strain on the heart leading to heart failure. Interestingly, however, recent evidence suggests that the impact of increased vessel stiffening extends beyond the tissue scale and can also have deleterious microscale effects on cellular function. Altered extracellular matrix (ECM) architecture has been recognized as a key component of the pre-atherogenic state. Here, the underlying causes of age-related vessel stiffening are discussed, focusing on age-related crosslinking of the ECM proteins as well as through increased matrix deposition. Methods to measure vessel stiffening at both the macro- and microscale are described, spanning from the pulse wave velocity measurements performed clinically to microscale measurements performed largely in research laboratories. Additionally, recent work investigating how arterial stiffness and the changes in the ECM associated with stiffening contributed to endothelial dysfunction will be reviewed. We will highlight how changes in ECM protein composition contribute to atherosclerosis in the vessel wall. Lastly, we will discuss very recent work that demonstrates endothelial cells (ECs) are mechano-sensitive to arterial stiffening, where changes in stiffness can directly impact EC health. Overall, recent studies suggest that stiffening is an important clinical target not only because of potential deleterious effects on the heart but also because it promotes cellular level dysfunction in the vessel wall, contributing to a pathological atherosclerotic state.
Evaluation of Four Approaches for "Sentiment Analysis on Movie Reviews": The Kaggle Competition

In this paper we present four different approaches for automatic sentiment classification on movie reviews. The proposed approaches: (a) statistical based, (b) bag-of-words based, (c) content based and (d) lexicon based approach, were evaluated in the "Sentiment Analysis on Movie Reviews" Kaggle competition. The competition results showed that each of the first three aforementioned approaches achieved approximately 65% sentiment classification accuracy, while the latter performed poorly compared to the others. The combination scheme of (b)-(c)-(d) proved to be our best performing set up achieving our best classification accuracy 67.931%, which ranked us in the 6th position among 861 participants.
Quiescent Thermal Emission from the Neutron Star in Aquila X-1

We report on the quiescent spectrum measured with Chandra ACIS-S of the transient, type I, X-ray-bursting neutron star Aql X-1, immediately following an accretion outburst. The neutron star radius, assuming a pure hydrogen atmosphere and a hard power-law spectrum, is R∞ = 13.4(d/5 kpc) km. Based on the historical outburst record of the Rossi X-Ray Timing Explorer All-Sky Monitor, the quiescent luminosity is consistent with that predicted by Brown, Bildsten, and Rutledge from deep crustal heating, lending support to this theory for providing a minimum quiescent luminosity of transient neutron stars. While not required by the data, the hard power-law component can account for 18% ± 8% of the 0.5-10 keV thermal flux. Short-timescale intensity variability during this observation is less than 15% rms (3 σ; 0.0001-1 Hz, 0.2-8 keV). Comparison between the Chandra spectrum and three X-ray spectral observations made between 1992 October and 1996 October find all spectra consistent with a pure H atmosphere, but with temperatures ranging from 145 to 168 eV, spanning a factor of 1.87 ± 0.21 in observed flux. The source of variability in the quiescent luminosity on long timescales (greater than years) remains a puzzle. If from accretion, then it remains to be explained why the quiescent accretion rate provides a luminosity so nearly equal to that from deep crustal heating.
Streaming Algorithms via Precision Sampling

A technique introduced by Indyk and Woodruff (STOC 2005) has inspired several recent advances in data-stream algorithms. We show that a number of these results follow easily from the application of a single probabilistic method called Precision Sampling. Using this method, we obtain simple data-stream algorithms that maintain a randomized sketch of an input vector $x=(x_1,x_2,\ldots,x_n)$, which is useful for the following applications:* Estimating the $F_k$-moment of $x$, for $k>2$. * Estimating the $\ell_p$-norm of $x$, for $p\in[1,2]$, with small update time. * Estimating cascaded norms $\ell_p(\ell_q)$ for all $p,q>0$. * $\ell_1$ sampling, where the goal is to produce an element $i$ with probability (approximately) $|x_i|/\|x\|_1$. It extends to similarly defined $\ell_p$-sampling, for $p\in [1,2]$. For all these applications the algorithm is essentially the same: scale the vector $x$ entry-wise by a well-chosen random vector, and run a heavy-hitter estimation algorithm on the resulting vector. Our sketch is a linear function of $x$, thereby allowing general updates to the vector $x$. Precision Sampling itself addresses the problem of estimating a sum $\sum_{i=1}^n a_i$ from weak estimates of each real $a_i\in[0,1]$. More precisely, the estimator first chooses a desired precision$u_i\in(0,1]$ for each $i\in[n]$, and then it receives an estimate of every $a_i$ within additive $u_i$. Its goal is to provide a good approximation to $\sum a_i$ while keeping a tab on the ``approximation cost'' $\sum_i (1/u_i)$. Here we refine previous work (Andoni, Krauthgamer, and Onak, FOCS 2010)which shows that as long as $\sum a_i=\Omega(1)$, a good multiplicative approximation can be achieved using total precision of only $O(n\log n)$.