Miracle-dz/UniFAR-FTUs huggingface dataset:
A Tale of Two Overhangs: The Nexus of Financial Sector and Sovereign Credit Risks

There has emerged in the Western economies a strong nexus between the credit risks of financial sectors and their sovereigns. We argue that this phenomenon can be understood in the context of two debt overhang problems: one affecting the financial sector due to its under-capitalisation following the crisis of 2007-08; the second, affecting the non-financial sector, whose incentives are crowded out by high sovereign debt and anticipated future taxes. While the desire to resolve the financial sector overhang may make bailouts tempting, they raise the risk of exacerbating the overhang related to sovereign debt. Conversely, reduction of growth prospects due to sovereign debt overhang can make the financial sector riskier as it is highly exposed to sovereign debt both through direct holdings and indirectly through implicit government guarantees. We provide evidence on this important nexus, based on our ongoing research that exploits data on European bank and sovereign credit risks.
Calculating Patient-specific Organ Doses from Adult Body CT Scans by Monte Carlo Analysis Using Male-Individual Voxel Phantoms

AbstractComputed tomography (CT) dose calculators such as the WAZA-ARI are useful for estimating the radiation dose from CT examination. This study determined correction coefficients for estimating organ doses to patients of any size attended to in daily clinical practice. To this end, the authors constructed voxel phantoms based on the CT images of patients of different size and simulated radiation transport in CT examinations to obtain organ doses using Monte Carlo simulation. The results show that the linear relationship between effective diameter and organ dose can predict patient-specific organ doses. The effective diameter-based prediction can provide accuracy within an error of ±10%, whereas an error of >20% was observed only in the liver and bladder. These results may contribute to practical irradiation dose calculation from a CT examination depending on individual patient size within a certain degree of accuracy.
Multicultural Experience Enhances Creativity

Many practices aimed at cultivating multicultural competence in educational and organizational settings (e.g., exchange programs, diversity education in college, diversity management at work) assume that multicultural experience fosters creativity. In line with this assumption, the research reported in this article is the first to empirically demonstrate that exposure to multiple cultures in and of itself can enhance creativity. Overall, the authors found that extensiveness of multicultural experiences was positively related to both creative performance (insight learning, remote association, and idea generation) and creativity-supporting cognitive processes (retrieval of unconventional knowledge, recruitment of ideas from unfamiliar cultures for creative idea expansion). Furthermore, their studies showed that the serendipitous creative benefits resulting from multicultural experiences may depend on the extent to which individuals open themselves to foreign cultures, and that creativity is facilitated in contexts that deemphasize the need for firm answers or existential concerns. The authors discuss the implications of their findings for promoting creativity in increasingly global learning and work environments.
Directional Neuronal Migration Is Impaired in Mice Lacking Adenomatous Polyposis Coli 2

Adenomatous polyposis coli 2 (APC2) is a family member of APC and mainly expressed in the nervous system. We previously reported that APC2 plays a critical role in axonal projection through the regulation of microtubule stability. Here, we show that a lack of Apc2 induces severe laminary defects in some regions of the mouse brain, including the cerebral cortex and cerebellum. In vivo BrdU labeling and immunohistochemical analyses with specific markers revealed that the laminary abnormalities are a result of dysregulated neuronal migration by a cell-autonomous mechanism. Using total internal reflection fluorescent microscopy, we found that APC2 is distributed along actin fibers as well as microtubules. Cerebellar granule cells in dissociated cultures and in vivo showed that BDNF-stimulated directional migration is impaired in Apc2-deficient neurons. We revealed that this impairment stems from the dysregulations of Rho family GTPase activation and TrkB localization, which disrupts the formation of BDNF-stimulated F-actin at the leading edge. Thus, APC2 is an essential mediator of the cytoskeletal regulation at leading edges in response to extracellular signals.
Paget's Disease of the Breast Areola Mimicking Cutaneous Melanoma

Abstract: Paget's disease of the breast nipple and areola complex represents a cutaneous manifestation of an underlying breast malignancy. The typical skin changes characteristic of breast Paget's disease are usually easily distinguished from those found of malignant melanoma. The rare finding of a pigmented cutaneous breast lesion mimicking melanoma presents a diagnostic dilemma. This case presentation describes a pigmented mammary lesion involving the areola that had undergone changes in diameter and pigmentation. The lesion was not associated with nipple drainage, nipple excoriation, dermal ulceration, or a palpable breast mass. The areola lesion was highly suggestive of a dermatologic malignancy such as malignant melanoma. As such, a biopsy was performed with initial findings suggestive of cutaneous melanoma. Upon more detailed analysis of a larger excision specimen, immunohistochemical staining was highly supportive of the diagnosis of breast Paget's disease. A total mastectomy with sentinel lymph node evaluation was performed along with immediate reconstruction. The final pathology revealed a small focus of ductal carcinoma in situ of the breast associated with the areola changes. Histologic and immunohistochemical analysis of a biopsy specimen were paramount in differentiating these two lesions.
Towards a context-specific theory of flourishing : explorations on the meaning, measurement, and policy implications of flourishing in higher education

Positive psychology has emerged as an antidote to the negativistic focus of mainstream psychology and human flourishing has recently been identified as its underlying aim. Flourishing is also the aim of positive education, an applied area of positive psychology that uses wellbeing theories and research on students in educational settings. Although there are several positive psychological theories of flourishing, a key limitation of these is “contextlessness,” or the tendency to neglect the role of context in flourishing. I argue there are three specific facets of contextlessness: in the conceptualisation of flourishing, in its measurement, and in the relationships between flourishing and its wider contexts. To address this gap, I present a series of exploratory studies aimed at contextualising flourishing. Higher education was selected as an appropriate setting for this work as positive education is limited at tertiary level and would benefit from contextualised understandings of flourishing. Findings of the studies – both qualitative and quantitative – provided novel and insightful understandings of flourishing as it is understood by students in higher education, aided the development and validation of a psychometric tool that measures context-specific flourishing in higher education, and enabled the exploration of flourishing in higher education in the political, economic, and cultural contexts within which higher education operates. Overall, results suggest flourishing in higher education is a complex and unique notion not always accountable by extant positive psychological theories. The thesis culminates in the proposition of a new theoretical framework, complementing extant theories, which enables flourishing to be understood and researched with greater recognition of the role of context. Further implications of the research and suggestions for the future are discussed with regard to the findings.
Resident satisfaction and its components in residential aged care.

PURPOSE The purpose of this study was to assess the direction and magnitude of the effects among the components of resident satisfaction in residential aged care and to examine if the relationships among satisfaction components vary according to facility type (i.e., nursing home and hostel). Briefly, a hostel is a low-care facility in which residents are more independent and receive personal but not nursing care. DESIGN AND METHODS A cross-sectional survey design was adopted to collect the required information, and a stratified random sampling approach was used to select facilities. Structural equation modeling was used to examine relationships among satisfaction components in a sample of 394 nursing home and 752 hostel residents. RESULTS The results indicate that satisfaction with staff care has a moderate and positive effect on all other aspects of resident satisfaction. The relationships among satisfaction components are different for nursing home and hostel residents. IMPLICATIONS The findings lead to an improved understanding of the interrelationship among resident satisfaction components, which has important implications for improving quality outcomes (e.g., resident satisfaction) through appropriate intervention (e.g., enhancing staff care).
The application of predictive functional control for permanent magnet synchronous motor servo system

The speed-regulation problem for permanent-magnet synchronous motor (PMSM) servo system is studied in this paper. The predictive functional control (PFC) theory applied to PMSM is concerned. This paper insists on how to implement the PFC method and what kind of performance can be expected from this technology. First, a simplified model of the PMSM servo system is accounted to refine this approach. Then, the controller based on standard PFC method is designed for speed-loop of the system. The optimal control law can be obtained by given a quadratic performance index. And a modified controller, i.e. PFC plus integrator, is also presented to regulate the effects of disturbances. Simulation and experimental comparisons with the PI control method are given to verify the effectiveness of the proposed method.
Large-scale content-based audio retrieval from text queries

In content-based audio retrieval, the goal is to find sound recordings (audio documents) based on their acoustic features. This content-based approach differs from retrieval approaches that index media files using metadata such as file names and user tags. In this paper, we propose a machine learning approach for retrieving sounds that is novel in that it (1) uses free-form text queries rather sound sample based queries, (2) searches by audio content rather than via textual meta data, and (3) can scale to very large number of audio documents and very rich query vocabulary. We handle generic sounds, including a wide variety of sound effects, animal vocalizations and natural scenes. We test a scalable approach based on a passive-aggressive model for image retrieval (PAMIR), and compare it to two state-of-the-art approaches; Gaussian mixture models (GMM) and support vector machines (SVM). We test our approach on two large real-world datasets: a collection of short sound effects, and a noisier and larger collection of user-contributed user-labeled recordings (25K files, 2000 terms vocabulary). We find that all three methods achieved very good retrieval performance. For instance, a positive document is retrieved in the first position of the ranking more than half the time, and on average there are more than 4 positive documents in the first 10 retrieved, for both datasets. PAMIR completed both training and retrieval of all data in less than 6 hours for both datasets, on a single machine. It was one to three orders of magnitude faster than the competing approaches. This approach should therefore scale to much larger datasets in the future.
Space-time FLAVORS: finite difference, multisymlectic, and pseudospectral integrators for multiscale PDEs

We present a new class of integrators for stiff PDEs. These integrators are generalizations of FLow AVeraging integratORS (FLAVORS) for stiff ODEs and SDEs introduced in [Tao, Owhadi and Marsden 2010] with the following properties: (i) Multiscale: they are based on flow averaging and have a computational cost determined by mesoscopic steps in space and time instead of microscopic steps in space and time; (ii) Versatile: the method is based on averaging the flows of the given PDEs (which may have hidden slow and fast processes). This bypasses the need for identifying explicitly (or numerically) the slow variables or reduced effective PDEs; (iii) Nonintrusive: A pre-existing numerical scheme resolving the microscopic time scale can be used as a black box and easily turned into one of the integrators in this paper by turning the large coefficients on over a microscopic timescale and off during a mesoscopic timescale; (iv) Convergent over two scales: strongly over slow processes and in the sense of measures over fast ones; (v) Structure-preserving: for stiff Hamiltonian PDEs (possibly on manifolds), they can be made to be multi-symplectic, symmetry-preserving (symmetries are group actions that leave the system invariant) in all variables and variational.
Extending training dataset for face detector learning

Training classifiers for object detection requires large sets of positive and negative samples and usually a large amount of computational time. Obtaining negative samples is not so difficult; it can be almost any pictures not containing the object of interest. Creating the positive training dataset is more challenging. Those pictures must contain the object we want to detect with corresponding annotation (information, where the object actually is). Especially creating the annotation is very time consuming. This paper describes several ways of extending training dataset used for training face detectors. Several experiments were concluded to evaluate the improvement of resulting classifier trained on datasets extended with the proposed methods. Web gallery Flickr provides many images suitable for our purposes, we tried to use such images with automatic annotation for training. We also focused on the possibility of using datasets from controlled environment. Finally, random transformations were applied on the annotated data to extend total samples count. We also explore the possibility of reducing training time by sampling the training set with Unique Importance Sampling.
Computing Similarity between Cultural Heritage Items using Multimodal Features

A significant amount of information about Cultural Heritage artefacts is now available in digital format and has been made available in digital libraries. Being able to identify items that are similar would be useful for search and navigation through these data sets. Information about items in these repositories is often multimodal, such as pictures of the artefact and an accompanying textual description. This paper explores the use of information from these various media for computing similarity between Cultural Heritage artefacts. Results show that combining information from images and text produces better estimates of similarity than when only a single medium is considered.
Performance of Exchange Rate Forecast Using Distance-Based Fuzzy Time Series

Fuzzy time series model has been employed by many researchers in forecasting activities such as students' enrolment, temperature fluctuations and stock prices. The existing fuzzy time series models require exact match of the fuzzy logic relationships to calculate the forecasted value. However, in real life applications, the exact match of fuzzy logic relationships is not possible. Thus, an improved fuzzy time series model termed as distance-based fuzzy time series model was proposed to remedy this shortcoming and successfully tested to the case of exchange rate data of New Taiwan Dollar (NTD) against United States Dollar (USD). The model was reportedly outperformed the artificial neural network and random walk models for the NTD against USD exchange rate. However, the performance of exchange rate using the distance-based fuzzy times series model for other currencies is still not fully explored. This paper forecasts the exchange rate of Malaysian Ringgit (MYR) against USD and tests the performance of the exchange rate using a distance-based fuzzy time series model. Data of the exchange rate USD against MYR from 11 August 2009 to 15 September 2009 were tested to the forecasting model. A sample of performance comparison between data sets of MYR against USD and NTD against USD was conducted. Under the same forecasting model, it is found that the forecasting errors for MYR against USD were smaller than NTD against USD exchange rate. The experiment results show that the forecasted exchange rate of MYR against USD has performed better under the distance-based fuzzy time series model. Keyword- Fuzzy time series, Exchange rate, Euclidean distance, Fuzzy rules, Forecasting error
Hierarchy of Regions of Amino Acid Sequence with Respect to Their Role in the Protein Spatial Structure

The method of the representation of amino acid sequence by graph of the interactions energy between parts of spatial structure has been elaborated. Our method provides the possibility to establish the compatibility between each point of a polypeptide chain and the Van der Waals interactions energy of regions of a native globule adjacent to this amino acid residue. We have undertaken an exhaustive analysis of a set of proteins. Boundaries of domain and module structures have been found. Nonequivalence of different parts of sequences in respect to their contribution to stabilization of the spatial structure of the protein macromolecules has been revealed. On the basis of the number of energetic levels which are necessary to identify all independent parts of the globule, the contribution from each part of the sequence to stabilization of the spatial structure of the globule is defined. Thus, it has been found that the sequence of amino acid residues coincides with the sequence of the numerical values which can be used in turn in formal procedures, such as an alignment, a search of consensus, the recognition of composition peculiarities, etc. An example of the comparison of proteins with various sequence identities is considered to demonstrate the scheme of an alignment procedure.
Bibliography on Path Analysis in Functional Neuroimaging

References for path analysis (structural equation modeling) and related connectivity analyses (“functional integration”) for functional neuroimaging are collected. This bibliography is part of a larger collection of bibliographies that was begun in 2001 see http://www.imm.dtu.dk/ fn/bib/Nielsen2001Bib/. The bibliography is written in LTEX and BIBTeX and should be available both as HTML, PDF and PostScript: • http://www.imm.dtu.dk/ fn/bib/Nielsen2001BibPath/ • http://www.imm.dtu.dk/ fn/bib/Nielsen2001BibPath.pdf • http://www.imm.dtu.dk/ fn/bib/Nielsen2001BibPath.ps The bibliography is probably far from complete, but new references are added whenever the author finds new material and has the time to add them. You can email the author if corrections are required or you have found some reference that you fell ought to be included: fn@imm.dtu.dk. Funding is from Lundbeck Foundation, the European Union project MAPAWAMO, International Neuroimaging Consortium (INC) HBM project, THOR Center for Neuroinformatics, the Villum Kann Rasmussen Foundation and the Lundbeck Foundation.
An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology

In order to establish a new personalized breast cancer screening paradigm, it is critically important to accurately predict the short-term risk of a woman having image-detectable cancer after a negative mammographic screening. In this study, we developed and tested a novel short-term risk assessment model based on deep learning method. During the experiment, a number of 270 “prior” negative screening cases was assembled. In the next sequential (“current”) screening mammography, 135 cases were positive and 135 cases remained negative. These cases were randomly divided into a training set with 200 cases and a testing set with 70 cases. A deep learning based computer-aided diagnosis (CAD) scheme was then developed for the risk assessment, which consists of two modules: adaptive feature identification module and risk prediction module. The adaptive feature identification module is composed of three pairs of convolution-max-pooling layers, which contains 20, 10, and 5 feature maps respectively. The risk prediction module is implemented by a multiple layer perception (MLP) classifier, which produces a risk score to predict the likelihood of the woman developing short-term mammography-detectable cancer. The result shows that the new CAD-based risk model yielded a positive predictive value of 69.2% and a negative predictive value of 74.2%, with a total prediction accuracy of 71.4%. This study demonstrated that applying a new deep learning technology may have significant potential to develop a new short-term risk predicting scheme with improved performance in detecting early abnormal symptom from the negative mammograms.
The Rb/E2F Pathway Modulates Neurogenesis through Direct Regulation of the Dlx1/Dlx2 Bigene Cluster

During brain morphogenesis, the mechanisms through which the cell cycle machinery integrates with differentiation signals remain elusive. Here we show that the Rb/E2F pathway regulates key aspects of differentiation and migration through direct control of the Dlx1 and Dlx2 homeodomain proteins, required for interneuron specification. Rb deficiency results in a dramatic reduction of Dlx1 and Dlx2 gene expression manifested by loss of interneuron subtypes and severe migration defects in the mouse brain. The Rb/E2F pathway modulates Dlx1/Dlx2 regulation through direct interaction with a Dlx forebrain-specific enhancer, I12b, and the Dlx1/Dlx2 proximal promoter regions, through repressor E2F sites both in vitro and in vivo. In the absence of Rb, we demonstrate that repressor E2Fs inhibit Dlx transcription at the Dlx1/Dlx2 promoters and Dlx1/2-I12b enhancer to suppress differentiation. Our findings support a model whereby the cell cycle machinery not only controls cell division but also modulates neuronal differentiation and migration through direct regulation of the Dlx1/Dlx2 bigene cluster during embryonic development.
Stochastic Stability of Continuous Time Consensus Protocols

A unified approach to studying convergence and stochastic stability of continuous time consensus protocols (CPs) is presented in this work. Our method applies to networks with directed information flow; both cooperative and noncooperative interactions; networks under weak stochastic forcing; and those whose topology and strength of connections may vary in time. The graph theoretic interpretation of the analytical results is emphasized. We show how the spectral properties, such as algebraic connectivity and total effective resistance, as well as the geometric properties, such the dimension and the structure of the cycle subspace of the underlying graph, shape stability of the corresponding CPs. In addition, we explore certain implications of the spectral graph theory to CP design. In particular, we point out that expanders, sparse highly connected graphs, generate CPs whose performance remains uniformly high when the size of the network grows unboundedly. Similarly, we highlight the benefits of using random versus regular network topologies for CP design. We illustrate these observations with numerical examples and refer to the relevant graph-theoretic results. Keywords: consensus protocol, dynamical network, synchronization, robustness to noise, algebraic connectivity, effective resistance, expander, random graph