Miracle-dz/UniFAR-FTUs huggingface dataset:
On the continuum limit of the conformal matrix models

The double scaling limit of a new class of the multi-matrix models proposed in [1], which possess the W-symmetry at the discrete level, is investigated in details. These models are demonstrated to fall into the same universality class as the standard multi-matrix models. In particular, the transformation of the W-algebra at the discrete level into the continuum one of the paper [2] is proposed, the corresponding partition functions being compared. All calculations are demonstrated in full in the first non-trivial case of W(3)-constraints.
A Modular Theory of Feature Learning

Learning representations of data, and in particular learning features for a subsequent prediction task, has been a fruitful area of research delivering impressive empirical results in recent years. However, relatively little is understood about what makes a representation `good'. We propose the idea of a risk gap induced by representation learning for a given prediction context, which measures the difference in the risk of some learner using the learned features as compared to the original inputs. We describe a set of sufficient conditions for unsupervised representation learning to provide a benefit, as measured by this risk gap. These conditions decompose the problem of when representation learning works into its constituent parts, which can be separately evaluated using an unlabeled sample, suitable domain-specific assumptions about the joint distribution, and analysis of the feature learner and subsequent supervised learner. We provide two examples of such conditions in the context of specific properties of the unlabeled distribution, namely when the data lies close to a low-dimensional manifold and when it forms clusters. We compare our approach to a recently proposed analysis of semi-supervised learning.
Feature Selection Using Decision Tree Induction in Class level Metrics Dataset for Software Defect Predictions

The importance of software testing for quality assurance cannot be over emphasized. The estimation of quality factors is important for minimizing the cost and improving the effectiveness of the software testing process. One of the quality factors is fault proneness, for which unfortunately there is no generalized technique available to effectively identify fault proneness. Many researchers have concentrated on how to select software metrics that are likely to indicate fault proneness. At the same time dimensionality reduction (feature selection of software metrics) also plays a vital role for the effectiveness of the model or best quality model. Feature selection is important for a variety of reasons such as generalization, performance, computational efficiency and feature interpretability. In this paper a new method for feature selection is proposed based on Decision Tree Induction. Relevant features are selected from the class level dataset based on decision tree classifiers used in the classification process. The attributes which form rules for the classifiers are taken as the relevant feature set or new feature set named Decision Tree Induction Rule based (DTIRB) feature set. Different classifiers are learned with this new data set obtained by decision tree induction process and achieved better performance. The performance of 18 classifiers is studied with the proposed method. Comparison is made with the Support Vector Machines (SVM) and RELIEF feature selection techniques. It is observed that the proposed method outperforms the other two for most of the classifiers considered. Overall improvement in classification process is also found with original feature set and reduced feature set. The proposed method has the advantage of easy interpretability and comprehensibility. Class level metrics dataset is used for evaluating the performance of the model. Receiver Operating Characteristics (ROC) and Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) error measures are used as the performance measures for checking effectiveness of the model.
Rivastigmine-loaded in situ gelling nanostructured lipid carriers for nose to brain delivery

Abstract In the current research work, rivastigmine (RV)-loaded in situ gelling nanostructured lipid carriers (NLCs) were developed for nose to brain delivery. NLCs were fabricated by ethanol injection method using glyceryl monosterate, Capmul MCM C8, Lecithin and Tween 80. NLCs showed average particle size of 123.2 ± 2.3 nm with entrapment efficiency of 68.34 ± 3.4%. DSC, XRD and IR studies showed complete amorphization and incorporation of the drug into nanoparticles. NLCs were incorporated into an in situ gelling system using 0.8% gellan gum and 15% Lutrol F 127. RV in situ gel showed excellent elasticity, rheology, mucoadhesion and adhesiveness to facilitate its adhesion to the upper nasal mucosa. NLC-based in situ gel showed a 2-fold increase in nasal permeation of the drug over plain RV solution. In situ gelling NLCs showed a 3-fold increase in enzyme inhibition efficacy.
Large Sparse Cone Non-negative Matrix Factorization for Image Annotation

Image annotation assigns relevant tags to query images based on their semantic contents. Since Non-negative Matrix Factorization (NMF) has the strong ability to learn parts-based representations, recently, a number of algorithms based on NMF have been proposed for image annotation and have achieved good performance. However, most of the efforts have focused on the representations of images and annotations. The properties of the semantic parts have not been well studied. In this article, we revisit the sparseness-constrained NMF (sNMF) proposed by Hoyer [2004]. By endowing the sparseness constraint with a geometric interpretation and sNMF with theoretical analyses of the generalization ability, we show that NMF with such a sparseness constraint has three advantages for image annotation tasks: (i) The sparseness constraint is more ℓ0-norm oriented than the ℓ1-norm-based sparseness, which significantly enhances the ability of NMF to robustly learn semantic parts. (ii) The sparseness constraint has a large cone interpretation and thus allows the reconstruction error of NMF to be smaller, which means that the learned semantic parts are more powerful to represent images for tagging. (iii) The learned semantic parts are less correlated, which increases the discriminative ability for annotating images. Moreover, we present a new efficient large sparse cone NMF (LsCNMF) algorithm to optimize the sNMF problem by employing the Nesterov’s optimal gradient method. We conducted experiments on the PASCAL VOC07 dataset and demonstrated the effectiveness of LsCNMF for image annotation.
Learning Behavior-Selection by Emotions and Cognition in a Multi-Goal Robot Task

The existence of emotion and cognition as two interacting systems, both with important roles in decision-making, has been recently advocated by neurophysiological research (LeDoux, 1998, Damasio, 1994. Following that idea, this paper presents the ALEC agent architecture which has both emotive and cognitive learning, as well as emotive and cognitive decision-making capabilities to adapt to real-world environments. These two learning mechanisms embody very different properties which can be related to those of natural emotion and cognition systems. The reported experiments test ALEC within a simulated autonomous robot which learns to perform a multi-goal and multi-step survival task when faced with real world conditions, namely continuous time and space, noisy sensors and unreliable actuators. Experimental results show that both systems contribute positively to the learning performance of the agent.
Migration and Racial Formations Among Somali Immigrants in North America

Early writers on migrants in North America such as Bryce-Laporte (1972) lamented the lack of research on black immigrants, referring to them as invisible immigrants. Since then, both the volume of research on foreign-born blacks as well as their share of the overall black population in North America have risen dramatically. This increase signals that the black population in North America is a diverse group increasingly identifying themselves more by culture and/or nationality than by skin colour. Using qualitative research methods, this project focuses on the disjuncture between how one set of black immigrants—Somalis—understand blackness in their homelands and how it is defined in North America. The findings reveal the problematics of racial categories and confirm the situational nature of racial identities. The results of this study are not substantially confined to African immigrants alone, but carry both theoretical and conceptual significance for the development, maintenance and consequences of racial formations in North America.
Preschool children's conceptions about the electric current and the functioning of electric appliances

SUMMARY In this work we describe a research project with 38 preschool children, aged 5.5 to 6.5, aiming at the detection and study of children's ideas and conceptions about the functioning of common electric appliances, and about the electric current and its properties. The investigation was realised in preschool premises, with semi-structured personal interviews and with the help of 15–19 cards, each showing an electric appliance. The analysis of the answers showed that children of this age have no difficulty in recognising and naming the electric appliances they are familiar with. As regards to the electric current, they hold a variety of preconceptions. They represent electric current in quite a static way, they think that electricity is included into electric appliances, wires, plugs/wall sockets or the power grid's pylons. They confound electric current and water flow, they believe that electricity outdoors (‘external’) is completely different from electricity indoors (‘internal’), and they have not formed any idea for the provenance, flow or movement of electric current. In contrast, they know quite well the hazards provoked by electricity, in the case that one puts his/her fingers into sockets. Those data will be taken into account in order to conceive and realise a proper didactical intervention in preschool education, aiming at helping children construct certain valid preconceptions about electric current, which could subsequently evolute to conceptions valid from a scientific point of view.
Mitral valve prolapse. Evolution and refinement of diagnostic techniques.

tant contribution in the evolution and refinement of diagnostic techniques that began when Barlow's angiographic observations provided the first rational basis for the origin of apical late systolic murmurs.3 Intracardiac phonocardiography subsequently recorded the clicks and late systolic murmur within the left atrium, a site appropriate for auscultatory events originating in the mitral apparatus.4 The advent of echocardiography catalyzed an explosion of interest,5 7 and a veritable epidemic of mitral valve prolapse ensued. M-mode records supplied a noninvasive method for detecting systolic mitral leaflet movement toward the left atrium,6,7 and two-dimensionally targeted M-mode echocardiography is still advocated.8 When twodimensional real-time imaging appeared upon the scene, the technique was heralded as ideally equipped to record the range of motion of the mitral leaflets relative to the annulus, and Doppler color flow interrogation soon furnished a sensitive means for detecting the presence and degree of mitral regurgitation.9 It rapidly became an article of faith that the diagnosis of mitral valve prolapse was established if the leaflets ascended above the plane of the annulus on two-dimensional imaging.10 This assumption has been called into question on at least two counts,9 first, by the disturbingly high frequency of the diagnosis of mitral prolapse in otherwise normal general populations,1" and, second, by the observation that mitral leaflets could appear to
Effects of obstacle sizes on wireless power transfer via magnetic resonance coupling

In this work, a comprehensive study on effects of obstacle sizes on WPT systems via magnetic resonance coupling is conducted by virtue of full-wave electromagnetic solutions. Five types of obstacles, i.e. Cu obstacles, ABS obstacles, earth obstacles, glass obstacles and sea-water obstacles, are discussed. It is shown that the sizes of ABS and glass obstacles have little effects on the efficiencies and resonant frequencies of WPT systems via magnetic resonance coupling. With the increasing sizes of earth obstacles, Cu obstacles and sea-water obstacles, the efficiencies of WPT systems will decrease and resonant frequencies will vary. The efficiencies will reduce more quickly with the increase of side lengths than the increase of thicknesses of Cu obstacles. The efficiencies always decrease dramatically with the increase of side lengths and thicknesses of sea-water obstacles. The results are also verified by experiments.
Methane Inhibition Alters the Microbial Community, Hydrogen Flow, and Fermentation Response in the Rumen of Cattle

Management of metabolic hydrogen ([H]) in the rumen has been identified as an important consideration when reducing ruminant CH4 emissions. However, little is known about hydrogen flux and microbial rumen population responses to CH4 inhibition when animals are fed with slowly degradable diets. The effects of the anti-methanogenic compound, chloroform, on rumen fermentation, microbial ecology, and H2/CH4 production were investigated in vivo. Eight rumen fistulated Brahman steers were fed a roughage hay diet (Rhode grass hay) or roughage hay:concentrate diet (60:40) with increasing levels (low, mid, and high) of chloroform in a cyclodextrin matrix. The increasing levels of chloroform resulted in an increase in H2 expelled as CH4 production decreased with no effect on dry matter intakes. The amount of expelled H2 per mole of decreased methane, was lower for the hay diet suggesting a more efficient redirection of hydrogen into other microbial products compared with hay:concentrate diet. A shift in rumen fermentation toward propionate and branched-chain fatty acids was observed for both diets. Animals fed with the hay:concentrate diet had both higher formate concentration and H2 expelled than those fed only roughage hay. Metabolomic analyses revealed an increase in the concentration of amino acids, organic, and nucleic acids in the fluid phase for both diets when methanogenesis was inhibited. These changes in the rumen metabolism were accompanied by a shift in the microbiota with an increase in Bacteroidetes:Firmicutes ratio and a decrease in Archaea and Synergistetes for both diets. Within the Bacteroidetes family, some OTUs assigned to Prevotella were promoted under chloroform treatment. These bacteria may be partly responsible for the increase in amino acids and propionate in the rumen. No significant changes were observed for abundance of fibrolytic bacteria, protozoa, and fungi, which suggests that fiber degradation was not impaired. The observed 30% decrease in methanogenesis did not adversely affect rumen metabolism and the rumen microbiota was able to adapt and redirect [H] into other microbial end-products for both diets. However, it is also required dietary supplements or microbial treatments to capture the additional H2 expelled by the animal to further improve rumen digestive efficiency.
Energy-Performance Modeling and Optimization of Parallel Computing in On-Chip Networks

This paper discusses energy-performance trade-off of networks-on-chip with real parallel applications. First, we propose an accurate energy-performance analytical model that conduct and analyze the impacts of both frequency-independent and frequency-dependent power. Second, we put together the communication overhead, memory access overhead, frequency scaling, and core count scaling to quantify the performance and energy consumed by NoCs. Third, we propose a new energy-performance optimization method, by choosing a pair of frequency and core count to get optimal energy or performance. Finally, we implement eight PARSEC parallel applications to evaluate our model and the optimization method. The experiment result confirms that our model predicts NoCs energy and performance well, and selects correct frequency level and core count for most parallel applications.
Interfacial Effects of MgO Buffer Layer on Perpendicular Anisotropy of$L1_0$FePt Films

MgO buffer layer in FePt perpendicular media blocked diffusion from the CrRu underlayer and promoted epitaxial growth of the FePt L1 0 film. The MgO layer deposited at 50degC appeared continuous and the interface roughness between MgO and FePt decreased with increasing thickness up to 4 nm. The predominant FePt (001) texture slightly improved with increasing MgO thickness. At 350degC, deposited MgO (2-nm thickness) was discontinuous and island-like. For 8-nm-thick MgO, the FePt (001) dominated and FePt (111) almost disappeared. Defects such as grain boundaries, twin boundaries, and interface roughness contributed to the high coercivity as a result of high deposition temperature or small MgO thickness
Supplemental macronutrients and microbial fermentation products improve the uptake and transport of foliar applied zinc in sunflower (Helianthus annuus L.) plants. Studies utilizing micro X-ray florescence

Enhancing nutrient uptake and the subsequent elemental transport from the sites of application to sites of utilization is of great importance to the science and practical field application of foliar fertilizers. The aim of this study was to investigate the mobility of various foliar applied zinc (Zn) formulations in sunflower (Helianthus annuus L.) and to evaluate the effects of the addition of an organic biostimulant on phloem loading and elemental mobility. This was achieved by application of foliar formulations to the blade of sunflower (H. annuus L.) and high-resolution elemental imaging with micro X-ray fluorescence (μ-XRF) to visualize Zn within the vascular system of the leaf petiole. Although no significant increase of total Zn in petioles was determined by inductively-coupled plasma mass-spectrometer, μ-XRF elemental imaging showed a clear enrichment of Zn in the vascular tissues within the sunflower petioles treated with foliar fertilizers containing Zn. The concentration of Zn in the vascular of sunflower petioles was increased when Zn was applied with other microelements with EDTA (commercial product Kick-Off) as compared with an equimolar concentration of ZnSO4 alone. The addition of macronutrients N, P, K (commercial product CleanStart) to the Kick-Off Zn fertilizer, further increased vascular system Zn concentrations while the addition of the microbially derived organic biostimulant “GroZyme” resulted in a remarkable enhancement of Zn concentrations in the petiole vascular system. The study provides direct visualized evidence for phloem transport of foliar applied Zn out of sites of application in plants by using μ-XRF technique, and suggests that the formulation of the foliar applied Zn and the addition of the organic biostimulant GroZyme increases the mobility of Zn following its absorption by the leaf of sunflower.
Newsletter – September 2006 A gentle overview of software visualisation

Software design is a realm of messy or “wicked” problems that are often too big, too ill-defined, and too complex for easy comprehension and solution (DeGrace and Stahl, 1998). Software itself is created, complex, abstract, and difficult to observe. Software is different from created physical artefacts, because it lacks their tangibility and visibility (e.g., What does a compiler look like? What is the size, weight and shape of an operating system? ). Code may be manifest, but how code works must be discovered and understood. Nevertheless, software interacts with objects in the physical world, often in complex and sophisticated ways. Software is dynamic, and software developers must reason not just about its properties, but about its behaviour – potentially complex behaviour – in time. Moreover, software is created and maintained in a social and organisational context which itself changes over time. Software ages as this social and organisational context evolves: teams change, knowledge decays, documentation falls out of date, intentions and rationale are forgotten over time (Ball & Eick, 1996).
The impact of changes in job strain and its components on the risk of depression.

OBJECTIVES We assessed the impact of changes in dimensions of the psychosocial work environment on risk of depression in a longitudinal cohort of Canadian workers who were free of depression when work conditions were initially reported. METHODS Using a sample (n = 3735) from the Canadian National Population Health Survey, we examined the effects of changes in job control, psychological demands, and social support over a 2-year period on subsequent depression. We adjusted models for a number of covariates, including personal history of depression. RESULTS Respondents with increased psychological demands were more likely to have depression over the following 2 years (odds ratio = 2.36; 95% confidence interval = 1.14, 4.88). This risk remained statistically significant after adjustment for age, gender, marital status, presence of children, level of education, chronic health conditions, subclinical depression when work conditions were initially assessed, family history of depression, and personal history of depression. CONCLUSIONS These results demonstrate that changes in psychological demands have a stronger influence than changes in job control on the onset of depression, highlighting the importance of not assuming an interaction between these 2 components of job strain when assessing health outcomes.
Emerging drugs for head and neck cancer

Head and neck squamous cell carcinoma is a devastating disease with poor outcomes in advanced stages. For patients with locally advanced disease, a multi-modality approach with chemotherapy and radiotherapy has been used. Despite advances in diagnosis and treatment, including improvements in radiation therapy, surgical techniques, chemotherapy and prevention strategies, survival rates for patients with recurrent head and neck cancer are poor. Several cytotoxic drugs with significant activities as single agents and/or combination regimens have shown high response rates, but over the past several years, significant improvement in survival has not been achieved. New drugs, including those that target the epidermal growth factor receptor, the p53 gene, RAS protein post-translational modification, the proteosome, vascular endothelial growth factor, cyclooxygenase-2 and other molecular pathways, are promising agents in the management of head and neck cancer. Their potential is being tested in various settings, including chemoprevention, recurrent and metastatic disease and combination with radiation therapy and/or cytotoxic agents.
Reality exists O.K.? An argument against constructivism and social constructionism

This paper reviews the way in which realism and constructivism have implicitly and explicitly underlain assumptions made by people in the helping professions about what can be known. Constructivism and the more recently rediscovered social constructionism have been appropriately influential in what is counted as knowledge in family therapy, but it is argued that they have gone too far in their assumption that a structured reality makes no contribution to what can be known. An alternative position is advocated, one which takes into account a relationship between the knower and the known. Some of the implications of such a co-constructivist position for family therapy are outlined.