Miracle-dz/UniFAR-FTUs huggingface dataset:
Communities of practice and PISA for Schools: Comparative learning or a mode of educational governance?

This paper examines the Organization for Economic Cooperation and Development’s (OECD) PISA for Schools , a new variant of the Programme for International Student Assessment (PISA) that compares school-level performance on reading, math and science with international schooling systems (e.g., Shanghai-China, Finland). Specifically, I focus here on a professional learning community – the Global Learning Network (GLN) – of U.S. schools and districts that have voluntarily participated in PISA for Schools, and how this, arguably, helps to normatively determine ‘what works’ in education. Drawing suggestively across diverse thinking around contemporary modes of governance, and emerging topological spaces and relations associated with globalization, and informed by interviews with 33 policy actors across the PISA for Schools policy cycle, my analyses suggest that GLN allows the OECD to discursively and normatively constrain how ‘world-class’ schools and systems, and their policies and practices, are defined. However, and in light of the productive capacities of power relations, I also argue that GLN provides opportunities for local educators and leaders to undertake meaningful collaboration and sharing, and to find policy spaces outside of those defined by more performative discursive framings of school accountability. To this end, I explore how GLN may help to foster alternative policy spaces from which educators can ‘talk back’ to national and state authorities, and potentially promote more ‘authentic’ understandings of, and possibilities for, schooling accountability.
On the Construction of (Explicit) Khodak's Code and Its Analysis

Variable-to-variable (VV) codes are very attractive yet not well understood data compression schemes. In 1972, Khodak claimed to provide upper and lower bounds for the achievable redundancy rate, however, he did not offer explicit construction of such codes. In this paper, we first present a constructive and transparent proof of Khodak's result showing that for memoryless sources there exists a code with the average redundancy bounded by D -5/3, where D is the average delay (e.g., the average length of a dictionary entry). We also describe an algorithm that constructs a VV length code with a small redundancy rate for large D. Then, we discuss several generalizations. We prove that the worst case redundancy does not exceed D -4/3. Furthermore, we provide similar upper bound for Markov sources (of order 1). Finally, we consider bounds that are valid for almost all memoryless and Markov sources for which the set of exceptional source parameters has zero measure. In particular, for all memoryless sources outside this exceptional class, we prove there exists a VV code with the average redundancy rate bounded by D -1-m/3+ epsiv and the worst case redundancy rate bounded by D -1-m/3+ epsiv, where m is the cardinality of the alphabet. We complete our analysis with a lower bound showing that for all VV codes the average and the worst case redundancy rates are at least D -2m-1- epsiv for almost all memoryless sources in the sense that the set of exceptional source parameters has zero measure. We prove these results using techniques of Diophantine approximations.
TraCount: a deep convolutional neural network for highly overlapping vehicle counting

We propose a novel deep framework, TraCount, for highly overlapping vehicle counting in congested traffic scenes. TraCount uses multiple fully convolutional(FC) sub-networks to predict the density map for a given static image of a traffic scene. The different FC sub-networks provide a range in size of receptive fields that enable us to count vehicles whose perspective effect varies significantly in a scene due to the large visual field of surveillance cameras. The predictions of different FC sub-networks are fused by weighted averaging to obtain a final density map. We show that TraCount outperforms the state of the art methods on the challenging TRANCOS dataset that has a total of 46796 vehicles annotated across 1244 images.
Formulation of Niosomal Gel for Enhanced Transdermal Lopinavir Delivery and Its Comparative Evaluation with Ethosomal Gel

The aim was to develop niosomal gel as a transdermal nanocarrier for improved systemic availability of lopinavir. Niosomes were prepared using thin-film hydration method and optimized for molar quantities of Span 40 and cholesterol to impart desirable characteristics. Comparative evaluation with ethosomes was performed using ex vivo skin permeation, fluorescence microscopy, and histopathology studies. Clinical utility via transdermal route was acknowledged using in vivo bioavailability study in male Wistar rats. The niosomal formulation containing lopinavir, Span 40, and cholesterol in a molar ratio of 1:0.9:0.6 possessed optimally high percentage of drug entrapment with minimum mean vesicular diameter. Ex vivo skin permeation studies of lopinavir as well as fluorescent probe coumarin revealed a better deposition of ethosomal carriers but a better release with niosomal carriers. Histopathological studies indicated the better safety profile of niosomes over ethosomes. In vivo bioavailability study in male Wistar rats showed a significantly higher extent of absorption (AUC0→∞, 72.87 h × μg/ml) of lopinavir via transdermally applied niosomal gel as compared with its oral suspension. Taken together, these findings suggested that niosomal gel holds a great potential of being utilized as novel, nanosized drug delivery vehicle for transdermal lopinavir delivery.
Examining the role of needs support in mediating the relationship between programme quality and developmental outcomes in youth sport†

Understanding how programme quality and needs support influence youth’s psychosocial development within sport programming is critical given the abundance of postulations, yet lack of empirical evidence, that recognise the potential influence these variables have on youth developmental outcomes. The purpose of this study was two-fold: (a) to examine the role that programme quality and basic needs support played in psychosocial outcomes in youth sport and (b) to investigate if basic needs support mediated the relationship between programme quality and psychosocial outcomes. It was hypothesised that programme quality and needs support would independently contribute to psychosocial outcomes and that programme quality would positively predict psychosocial outcomes and needs support would mediate this relationship. A total of 214 youth (Mage = 14.26, SD = 1.88; 61 male, 153 female) completed three questionnaires at two time points that assessed the study variables. To examine the first purpose, structural equation modelling was used. Programme quality and needs support (measured at Time 1) significantly predicted psychosocial outcomes (measured at Time 2) independently. To investigate the second study purpose, bootstrapping analysis was used to test if needs support mediated this relationship. Results indicated that needs support partially mediated the relationship between programme quality and psychosocial outcomes. Findings provide initial evidence of the importance of delivering high-quality programmes that support basic psychological needs in order to foster psychosocial development in youth. Applied implications and future research areas are discussed.
Utilising Creative Computing and data mining techniques to analyse queries in a meta-search system

As the World Wide Web has a huge number of information and update rapidly, the needs of people for searching information from the web increase dramatically. Meanwhile, most of current search engines can search a query from websites, but cannot present the most relevant results to users. The main challenge for search engines and meta search engines are how to describe and analyse a query more comprehensively. In order to solve this problem, this paper presents a meta-search engine named Personlised Meta Search Engine (PMSE) that has been used to adapt the web search processes to meet users' requirement. Firstly, a technique will enrich the given query by data mining and creative computing. Secondly, a new meta-search engine is developed to make the results more relevant. Finally, an example is used to demonstrate what the proposed approach will be described more comprehensively and to produce fewer but more precise answers to individual users.
The effects of the CEECS's accession on sectoral trade: A value added perspective

International fragmentation of production and economic integration change the structure of international trade. Novel datasets reveal how production processes are unbundled across borders and connected internationally through Global Value Chains (GVCs). Yet, the impact of Economic Integration Agreements (EIAs) on GVCs is still poorly understood. This paper investigates how the accession of 10 Central and Eastern European Countries (CEECs) to the European Union (EU) affected trade in value added in Europe. Using a gravity model of international trade for a sample of 40 countries over the period 1995-2009, we find that EU accession mainly fostered Eastern entrants' integration in other CEECs' value chains. Smaller integration benefits arise for East-West trade in services stemming from low-skill value adding activities.
Stereotyping and Acceleration – Gender, Procedural Acceleration and Marginalised Judicial Review in the Dutch Asylum System

When deciding which statements need to be proven, and when proof has been delivered; and when deciding which statements and which behaviour are considered credible, it is necessary to use certain normative standards. These standards are hard to make explicit, and most often they are not. In the first section of this chapter, I will show the subtle and blatant ways in which evidence assessment is shaped by gendered notions of interview officials, decision makers, and legal representatives of asylum seekers. The slippery nature of evidence assessment makes review and appeal procedures very important. Precisely because the issue is so problematic, the precise nature of the review or appeal procedure available to asylum seekers may well be decisive for the outcome of their procedure. In the second section, I will describe recent Dutch case law on proof and credibility, which allows for a considerable expansion of the use of the accelerated asylum procedure, and argue that this body of case law is at considerable tension with the European Convention on Human Rights. In a final section, I will draw the two strings of this chapter together. The stereotyping described in paragraph 1 will probably increase in the new Dutch asylum system, which makes the ‘exceptional’ accelerated procedure into the normal procedure. Because of time pressure, interviewers, decision makers and lawyers will be more prone to have recourse to stereotypes because stereotyping is helpful in allowing for quick decisions. Also, time pressure will make effective judicial control less likely. Abstracted from the particularities of the Dutch context, the main propositions of this chapter concern the necessarily constructed nature of flight motives; the contested nature of that construction; and the influence of the law on proof and credibility on the contested construction of flight motives. When discussing law on proof and credibility, I will address both Dutch domestic law and case law of the European Court of Human Rights. One aspect of the review of case law from the European Court will be a new reading of its decisions regarding Article 13 ECHR.
Nonthermal dark matter from cosmic strings

Cosmic strings can be created in the early universe during symmetry-breaking phase transitions, such as might arise if the gauge structure of the standard model is extended by additional U(1) factors at high energies. Cosmic strings presented in the early universe form a network of long horizon-length segments, as well as a population of closed string loops. The closed loops are unstable against decay, and can be a source of nonthermal particle production. In this work we compute the density of weakly-interacting massive particle dark matter formed by the decay of gauge theory cosmic string loops derived from a network of long strings in the scaling regime or under the influence of frictional forces. We find that for symmetry-breaking scales larger than 10{sup 10} GeV, this mechanism has the potential to account for the observed relic density of dark matter. For symmetry-breaking scales lower than this, the density of dark matter created by loop decays from a scaling string network lies below the observed value. In particular, the cosmic strings originating from a U(1) gauge symmetry broken near the electroweak scale, that could lead to a massive Z{sup '} gauge boson observable at the LHC, produces a negligibly small darkmore » matter relic density by this mechanism. « less
Using a Cellular Automaton to Extract Medical Information from Clinical Reports

An important amount of clinical data concerning the medical history of a patient is in the form of clinical reports that are written by doctors. They describe patients, their pathologies, their personal and medical histories, findings made during interviews or during procedures, and so forth. They represent a source of precious information that can be used in several applications such as research information to diagnose new patients, epidemiological studies, decision support, statistical analysis, and data mining. But this information is difficult to access, as it is often in unstructured text form. To make access to patient data easy, our research aims to develop a system for extracting information from unstructured text. In a previous work, a rule-based approach is applied to a clinical reports corpus of infectious diseases to extract structured data in the form of named entities and properties. In this paper, we propose the use of a Boolean inference engine, which is based on a cellular automaton, to do extraction. Our motivation to adopt this Boolean modeling approach is twofold: first optimize storage, and second reduce the response time of the entities extraction.
DEVELOPING EPISODIC SEMANTICS

In this paper, we describe a variant of the semantics of distinguishing criteria for artificial agents. A distinguishi ng criterion is a computational abstraction of meaning and is modeled by a locally tuned detector reacting to some part of its input space. All criteria are dynamic in that they are autonomously and incrementally (re)constructed during the life/runtime of the developing agent. We apply this semantic framework to learning from episodes describing both performed and observed actions in a virtual environment by “who did what to whom and with what result” theta role frames. Thanks to their mirror neuron-like nature and auto-associative property, the acquired criter ia enable the agent to plan actions and predict their results, infer internal states and possible actions of other agents, and open a route to situated pragmatic language use.
Recognition of the multi-specularity objects using the eigen-window

This paper describes a method for recognizing partially occluded specularity objects for bin-picking tasks using the eigen-space analysis. Although effective in recognizing an isolated object, as was shown by Murase and Nayar, the current method can not be applied to partially occluded objects that are typical in bin-picking tasks. The analysis also requires that the object is centered in an image before recognition. These limitations of the eigen-space analysis are due to the fact that the whole appearance of an object is utilized as a template for the analysis. We propose a new method, referred to as the "eigen-window" method, that stores multiple partial appearances of an object in the eigen-space. Such partial appearances require a large number of memory space. To reduce the memory requirement by avoiding redundant windows and to select only effective windows to be stored, a similarity measure among windows is developed. Using a pose clustering method among windows, the method determines the pose of an object. We have implemented the method and verify the validity of the method.
Unsupervised symmetry detection : A network which learns from single examples

Learning problems of higher order, like detecting an unknown symmetry within a pattern, are difficult tasks for most neural networks. Even for small problem sizes a very large number of training examples is needed. We propose a self-organizing network which learns unsupervised to categorize the symmetry of new input patterns according to symmetries already seen. Single examples of a given symmetry are sufficient for learning. The network achieves a classification reliability of 96% percent (3 classes). The underlying mechanism is based on the self-organization of dynamical links which is driven by correlated activity of cell assemblies. Introduction. Neural networks are widely used for tasks where a high dimensional input pattern has to be classified according to a few possible output classes. They have proven to be very successful in cases where the rules for the input–output mapping are complicated or even not known [1, 2]. Consider for example the famous NetTalk system, a network which learns from examples to translate written English text into the proper phonemes. The identification of the right phoneme depends partly on the actual letter (problem of order one), but partly also on the context, i.e. the letters before and after that letter (problem of order two or higher). Standard neural networks have been especially successful in this hybrid situation. But what happens, if the problem is of an order strictly higher than one, that is, none of the possible input examples allows classification from looking at only one input cell? As an example one may consider the detection of global symmetries in a 2D-pattern, i. e. mirror symmetry with respect to an arbitrary axis (Fig. 1). Here the interesting information resides only in relations among the local features and not in the features themselves. Many problems in vision or invariant pattern recognition are of this kind. It has been shown [4] that a standard neural network algorithm (Boltzmann machine) can solve such a problem for small problem sizes (4 × 4 or 10 × 10 pixels). This network has 12 hidden unit neurons with initially homogeneous connections to all input cells. Thus it has no a priori knowledge about the learning task. During learning, the hidden units develop weight patterns with the same symmetries as those of the shown examples. However, the number of examples needed to train the network is very large (several 10) and grows rapidly with the problem size. It has been suggested in [4] that the order of the problem rather than its size is the appropriate measure for the difficulty of training a network to solve this problem. The ∗Supported by grants from the German Ministry for Science and Technology (413-5839-01 IN 101 B/9), and a research grant from the Human Frontier Science Program. A formal definition of the order of a problem was first given by Minsky and Papert [3].
Effects of mass media campaign exposure intensity and durability on quit attempts in a population-based cohort study.

OBJECTIVE To assess the extent to which intensity and timing of televised anti-smoking advertising emphasizing the serious harms of smoking influences quit attempts. METHODS Using advertising gross rating points (GRPs), we estimated exposure to tobacco control and nicotine replacement therapy (NRT) advertising in the 3, 4-6, 7-9 and 10-12 months prior to follow-up of a replenished cohort of 3037 Australian smokers during 2002-08. Using generalized estimating equations, we related the intensity and timing of advertising exposure from each source to the likelihood of making a quit attempt in the 3 months prior to follow-up. RESULTS Tobacco control advertising in the 3-month period prior to follow-up, but not in more distant past periods, was related to a higher likelihood of making a quit attempt. Each 1000 GRP increase per quarter was associated with an 11% increase in making a quit attempt [odds ratio (OR) = 1.11, 95% confidence interval (CI) 1.03-1.19, P = 0.009)]. NRT advertising was unrelated to quit attempts. CONCLUSIONS Tobacco control advertising emphasizing the serious harms of smoking is associated with short-term increases in the likelihood of smokers making a quit attempt. Repeated cycles of higher intensity tobacco control media campaigns are needed to sustain high levels of quit attempts.
Inhibition of Chemokine (C-C Motif) Receptor 7 Sialylation Suppresses CCL19-Stimulated Proliferation, Invasion and Anti-Anoikis

Chemokine (C-C motif) receptor 7 (CCR7) is involved in lymph-node homing of naive and regulatory T cells and lymphatic metastasis of cancer cells. Sialic acids comprise a group of monosaccharide units that are added to the terminal position of the oligosaccharide chain of glycoproteins by sialyation. Recent studies suggest that aberrant sialylation of receptor proteins contributes to proliferation, motility, and drug resistance of cancer cells. In this study, we addressed whether CCR7 is a sialylated receptor protein and tried to elucidate the effect of sialylation in the regulation of signal transduction and biological function of CCR7. Our results demonstrated that α-2, 3-sialyltransferase which catalyze sialylation reaction in vivo was overexpressed in breast tumor tissues and cell lines. Lectin blot analysis clearly demonstrated that CCR7 receptor was sialyated in breast cancer cells. Chemokine (C-C motif) ligand 19 (CCL19), the cognate ligand for CCR7, induced the activation of extracellular signal-regulated kinase (ERK) and AKT signaling and increased the expression of cell cycle regulatory proteins and proliferation of breast cancer cells. When cells were pre-treated with a sialyltransferase inhibitor AL10 or sialidase, CCL19-induced cell growth was significantly suppressed. CCL19 also increased invasion and prevented anoikis by up-regulating pro-survival proteins Bcl-2 and Bcl-xL. Inhibition of sialylation by AL10 totally abolished these effects. Finally, we showed that AL10 inhibited tumorigenicity of breast cancer in experimental animals. Taken together, we demonstrate for the first time that CCR7 receptor is a sialylated protein and sialylation is important for the paracrine stimulation by its endogenous ligand CCL19. In addition, inhibition of aberrant sialylation of CCR7 suppresses proliferation and invasion and triggers anoikis in breast cancer cells. Targeting of sialylation enzymes may be a novel strategy for breast cancer treatment.