Miracle-dz/UniFAR-FTUs huggingface dataset:
A Nonparametric Method for Predicting Survival Probabilities

Public programs often use statistical profiling to assess the risk that applicants will become long-term dependent on the program. The literature uses linear probability models and (Cox) proportional hazard models to predict duration outcomes. These either focus on one threshold duration or impose proportionality. In this paper we propose a nonparametric weighted survivor prediction method where the weights depend on the distance in characteristics between individuals. A simulation study shows that an Epanechnikov weighting function with a small bandwidth gives the best predictions while the choice of distance function is less important for the performance of the weighted survivor prediction method. This yields predictions that are slightly better than Cox survivor function predictions. In an empirical application concerning the outflow to work from unemployment insu rance, we do not find that the weighting method outperforms Cox survivor function predictions.
Music and language side by side in the brain: a PET study of the generation of melodies and sentences

Parallel generational tasks for music and language were compared using positron emission tomography. Amateur musicians vocally improvised melodic or linguistic phrases in response to unfamiliar, auditorily presented melodies or phrases. Core areas for generating melodic phrases appeared to be in left Brodmann area (BA) 45, right BA 44, bilateral temporal planum polare, lateral BA 6, and pre‐SMA. Core areas for generating sentences seemed to be in bilateral posterior superior and middle temporal cortex (BA 22, 21), left BA 39, bilateral superior frontal (BA 8, 9), left inferior frontal (BA 44, 45), anterior cingulate, and pre‐SMA. Direct comparisons of the two tasks revealed activations in nearly identical functional brain areas, including the primary motor cortex, supplementary motor area, Broca's area, anterior insula, primary and secondary auditory cortices, temporal pole, basal ganglia, ventral thalamus, and posterior cerebellum. Most of the differences between melodic and sentential generation were seen in lateralization tendencies, with the language task favouring the left hemisphere. However, many of the activations for each modality were bilateral, and so there was significant overlap. While clarification of this overlapping activity awaits higher‐resolution measurements and interventional assessments, plausible accounts for it include component sharing, interleaved representations, and adaptive coding. With these and related findings, we outline a comparative model of shared, parallel, and distinctive features of the neural systems supporting music and language. The model assumes that music and language show parallel combinatoric generativity for complex sound structures (phonology) but distinctly different informational content (semantics).
Fixed-Term Homotopy

A new tool for the solution of nonlinear differential equations is presented. The Fixed-Term Homotopy (FTH) delivers a high precision representation of the nonlinear differential equation using only a few linear algebraic terms. In addition to this tool, a procedure based on Laplace-Pade to deal with the truncate power series resulting from the FTH method is also proposed. In order to assess the benefits of this proposal, two nonlinear problems are solved and compared against other semianalytic methods. The obtained results show that FTH is a power tool capable of generating highly accurate solutions compared with other methods of literature.
Pre-transplant Evaluation of Donor Urinary Biomarkers can Predict Reduced Graft Function After Deceased Donor Kidney Transplantation

AbstractSeveral recipient biomarkers are reported to predict graft dysfunction, but these are not useful in decision making for the acceptance or allocation of deceased donor kidneys; thus, it is necessary to develop donor biomarkers predictive of graft dysfunction. To address this issue, we prospectively enrolled 94 deceased donors and their 109 recipients who underwent transplantation between 2010 and 2013 at 4 Korean transplantation centers. We investigated the predictive values of donor urinary neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), and L-type fatty acid binding protein (L-FABP) for reduced graft function (RGF). We also developed a prediction model of RGF using these donor biomarkers. RGF was defined as delayed or slow graft function. Multiple logistic regression analysis was used to generate a prediction model, which was internally validated using a bootstrapping method. Multiple linear regression analysis was used to assess the association of biomarkers with 1-year graft function. Notably, donor urinary NGAL levels were associated with donor AKI (P = 0.014), and donor urinary NGAL and L-FABP were predictive for RGF, with area under the receiver-operating characteristic curves (AUROC) of 0.758 and 0.704 for NGAL and L-FABP, respectively. The best-fit model including donor urinary NGAL, L-FABP, and serum creatinine conveyed a better predictive value for RGF than donor serum creatinine alone (P = 0.02). In addition, we generated a scoring method to predict RGF based on donor urinary NGAL, L-FABP, and serum creatinine levels. Diagnostic performance of the RGF prediction score (AUROC 0.808) was significantly better than that of the DGF calculator (AUROC 0.627) and the kidney donor profile index (AUROC 0.606). Donor urinary L-FABP levels were also predictive of 1-year graft function (P = 0.005). Collectively, these findings suggest donor urinary NGAL and L-FABP to be useful biomarkers for RGF, and support the use of a new scoring system based on donor biomarkers to facilitate decision-making in acceptance and allocation of deceased donor kidneys and contribute to maximal organ utilization.
Gamma-Ray Bursts from Baryon Decay in Neutron Stars

The standard unbroken electroweak theory is known to erase the baryon number. The baryon number symmetry can be restored in the core of a neutron star if its density diverges via gravitational instability due to a binary merger event. We argue that for certain double Higgs models with discrete symmetries this process may result in an expanding self-sustained burning front that would convert the entire neutron matter into radiation. This process would release ~1054 ergs in electromagnetic radiation over ~10-4 s with negligible baryonic contamination. The resulting fireball would have all of the properties necessary to produce a γ-ray burst as a result of its interaction with ambient interstellar gas. The subsequent Higgs decay would produce a millisecond burst of ~1052 ergs in ~100 GeV neutrinos, which might be observable. The above mechanism may also cause electroweak baryogenesis in the early universe, which gives rise to the observed matter-antimatter asymmetry today.
Towards Semantic Web Documents Retrieval through Ontology Mapping: Preliminary Results

The development of the Semantic Web has led to the proposal of several solutions concerning the retrieval of Semantic Web Documents (SWDs). However, current solutions presuppose that the query is given in a structured way using a formal language and provide no advanced means for the (semantic) alignment of the query to the contents of the Semantic Web Documents. As a consequence, many of the Web users can not take advantage of the semantic retrieval services provided, given their inability to express formal queries and/or their divergent conceptualizations of a domain. In this paper, we report on preliminary experiments towards an approach to SWDs retrieval that aims to support users to form semantic queries – requiring no knowledge and skills for expressing queries in a formal language and to retrieve SWDs whose content is similar to the queries formed.
What motivates chinese consumers to adopt FinTech services: a regulatory focus theory

FinTech is a newly emerged service that combines innovative financial services and latest mobile technology. Since this service is new, there is a lack of study that investigates consumer behavior in adopting the service. Founded on Regulatory Focus Theory, our study aims to explain two different factors that motivate the adoption of FinTech in China. We plan to collect data from university students who used Alipay and Tenpay (two of the leading FinTech companies in China). The findings from our study will be valuable to FinTech companies in strategizing and promoting usage of their services.
The Complexity of Resource-Bounded Propositional Proofs

Propositional Proof Complexity is an active area of research whose main focus is the study of the length of proofs in propositional logic. There are several motivations for such a st udy, the main of which is probably its connection to the P vs NP problem in Computational Complexity. The experience in the field has revealed that the most interesting paramete of a proof system is the set of allowed formulas. The exact set of rules and axioms of the system i s often irrelevant as long as they remain sound and reasonable. This parameterization by the set of allowed formulas establishes a link with the field of Boolean complexity where bounds are imposed on di fferent computational resources as a classification tool. This idea is adopted in the proof complexity setting too. In this thesis we study the complexity of several ’resource-bounded’ pr oof systems. The first set of results of the thesis is about a resource-bounded proof system that we c all th Monotone Sequent Calculus (MLK). This is the standard propositional Gentzen Calculus (LK) when negation is not allowed in the formulas. The main result is that the use of ne gation does not yield exponential savings in the length of proofs. More precisely, we show that MLK qua sipolynomially simulates LK on monotone sequents. We also show that, as refutation sys tem , MLK is polynomially bounded if and only if LK is. These results are in sharp contrast wi th the situation in Boolean complexity where the use of negations provably yields an exponential ga p in computational power. The second set of results is about a proof system extending Resolution by allowing disj unctions of conjunctions of two literals and not only disjunctions of literals. We prove t hat the Weak Pigeonhole Principle with twice as many pigeons than holes, and random 3-CNF formula s, require exponential-size proofs in this system. The interest of our results is that we bre ak th barrier of one alternation of disjunctions and conjunctions in the context of the weak pigeonhole principle and random 3-CNF formulas for the first time. The techniques that we employ combine old ideas with new arguments using powerful tools from probability theory. The third and final set of results is about the exact complexity of the Weak Pigeonhole P rinciple with twice as many pigeons than holes. We show that all previously known quasipolynomi al-size proofs of bounded-depth are not optimal in terms of size. As a consequence of our result, we obt ain Bounded Arithmetic proofs that there are infinitely many primes from ’large numbe r assumptions’ that are provably weaker than previously needed.
Covert Channel Detection framework for cloud using distributed machine learning

Recent research shows that colluded malware in different VMs sharing physical host may use access latency of a resource as a covert channel to leak critical information. Covert channels employ time characteristics to transmit confidential information to attackers. In this manuscript we have made two important contributions and to the best of our knowledge they are novel. One is to propose a framework for detecting covert channel attack based anomalies in cloud using machine learning . This framework is based on application criticality where the degree of error tolerance will be based on the sensitivity of application. This is a useful feature for cloud based applications as SLAs are trade off between service and cost. There are two modules of the framework, a cluster monitor and another module becomes part of hypervisor for monitoring purposes. Second contribution is we propose an approach to distribute the machine learning technique in such a manner to be able to handle imbalance learning as well as enabling SVM to handle large datasets.
Solar system constraints on Gauss–Bonnet mediated dark energy

Although the Gauss–Bonnet term is a topological invariant for general relativity, it couples naturally to a quintessence scalar field, modifying gravity at solar system scales. We determine the solar system constraints due to this term by evaluating the post-Newtonian metric for a distributional source. We find a mass-dependent, 1/r7 correction to the Newtonian potential, and also deviations from the Einstein gravity prediction for light-bending. We constrain the parameters of the theory using planetary orbits, the Cassini spacecraft data, and a laboratory test of Newton’s law, always finding extremely tight bounds on the energy associated to the Gauss–Bonnet term. We discuss the relevance of these constraints to late-time cosmological acceleration.
IDS: An Immune-Inspired Approach for the Detection of Software Design Smells

We propose a parallel between object-oriented system designs and living creatures. We suggest that, like any living creature, system designs are subject to diseases, which are design smells (code smells and anti patterns). Design smells are conjectured in the literature to impact the quality and life of systems and, therefore, their detection has drawn the attention of both researchers and practitioners with various approaches. With our parallel, we propose a novel approach built on models of the immune system responses to pathogenic material. We show that our approach can detect more than one smell at a time. We build and test our approach on Gantt Project v1.10. 2 and Xerces v2.7. 0, for which manually-validated and publicly available smells exist. The results show a significant improvement in detection time, precision, and recall, in comparison to the state–of–the–art approaches.
Pre-service teachers’ use of metaphors for mentoring relationships

Abstract The purpose of this study was to examine pre-service teachers’ and their mentor teachers’ metaphorical images of their mentoring relationships and the extent to which the participants changed their metaphors as they went through their practicum experience. Three rounds of interviews were conducted with seven secondary pre-service teachers over a one-year Graduate-Diploma of Education Course; at the outset of the programme, at the end of the first placement and at the end of the second placement. Thirteen Mentor teachers were also interviewed at the beginning and end of each placement. The findings indicated that the metaphors the two groups initially constructed significantly overlapped and focused on interpersonal relationships and providing guidance and support. Also, some changes were observed in the metaphors used by the two groups depending on their mentoring experiences. Implications for mentor teachers and teacher education are discussed.
Data Reduction by Genetic Algorithms and Non-Algebraic Feature Construction: A Case Study

Real-world data are often prepared for purposes other than data mining and machine learning and, therefore, are represented by primitive attributes. When data representation is primitive, preprocessing data before looking for patterns becomes necessary. If lack of domain experts prevents the use of highly informative attributes, patterns are hard to uncover due to complex attribute interactions. This article suggests a new use of MFE3/GA to restructure the primitive data representation by means of capturing and compacting hidden information into new features in order to highlight them to the learner. Empirical results on Poker Hand data set show that the new use successfully improves learning this concept by means of data reduction, generation of a smaller decision tree classifier, and accuracy improvement.
Managed Mixtures of Aspen and White Spruce 21 to 25 Years after Establishment

Intimate mixtures of trembling aspen (Populus tremuloides Michx.) and white spruce (Picea glauca (Moench) Voss) are a key feature of western Canadian boreal forests. These mixtures have the potential to produce high yields of merchantable fibre and provide numerous ecological services. Achievement of this potential has been difficult, and often expensive, to realize as a regeneration goal in managed forests. We report 21 to 25 year results of managed mixtures on two study sites where the white spruce was planted, and the density of aspen natural regeneration manipulated within five years of the stand initiation disturbance. On both sites, white spruce mortality did not increase with increasing aspen density. While height and diameter growth of white spruce declined with increasing aspen density, the effect was not entirely consistent across the two sites. Abrasion from aspen branches was the most common source of damage to spruce crowns. Mixed stands had greater merchantable volume production than pure spruce stands based on model projections. Application of aspen harvest at year 60, while protecting the spruce component for a second harvest entry at year 90, was projected to optimize combined yield for the mixedwood stands.
Geometry-Based Image Retrieval in Binary Image Databases

In this paper, a geometry-based image retrieval system is developed for multiobject images. We model both shape and topology of image objects using a structured representation called curvature tree (CT). The hierarchy of the CT reflects the inclusion relationships between the image objects. To facilitate shape-based matching, triangle-area representation (TAR) of each object is stored at the corresponding node in the CT. The similarity between two multiobject images is measured based on the maximum similarity subtree isomorphism (MSSI) between their CTs. For this purpose, we adopt a recursive algorithm to solve the MSSI problem and a very effective dynamic programming algorithm to measure the similarity between the attributed nodes. Our matching scheme agrees with many recent findings in psychology about the human perception of multiobject images. Experiments on a database of 13,500 real and synthesized medical images and the MPEG-7 CE-1 database of 1,400 shape images have shown the effectiveness of the proposed method.
Simple Fuzzy Rule Based Edge Detection

Abstract ² Most of the edge detecti on methods available in literature are gradient based , which further apply thresholding , to find the final edge map in an image. In this paper, we propose a novel method that is based on fuzzy logic for edge detection in gray images without using the gradi ent and thresholding. Fuzzy logic is a mathematical logic that attempts to solve problems by assigning values to an imprecise spectrum of data in order to arrive at the most accurate conclusion possible. Here, the fuzzy logic is used to conclude whether a pixel is an edge pixel or not. The proposed technique begins by fuzzifying the gray values of a pixel into two fuzzy variables , namely the black and the white . Fuzzy rules are defined to find the edge pixels in the fuzzified image. The resultant edge map m ay contain some extraneous edges, which are further removed from the edge map by separately examining the intermediate intensity range pixels. Finally , the edge map is improved by finding some left out edge pixels by defining a new membership function for the pixels that have their entire 8 -neighbourhood pixel s classified as white. We have compared our proposed method with some of the existing standard edge detector operators that are available in the literature on image processing. The quantitative analysi s of the proposed method is given in term s of entropy value. Keywords ² Edge detection, Edge improvement, Fuzzy rules, Membership function.
Keeping the faith: the role of religion and faith communities in preventing teen pregnancy.

This two-chapter report Keeping the Faith: The Role of Religion and Faith Communities in Preventing Teen Pregnancy addresses what’s behind some of those barriers examines what research says about the role religion plays in teens’ decisions about sex and makes the argument for increased cooperation and understanding among secular and faith communities. In chapter one social critic Barbara Dafoe Whitehead offers a reflection on overcoming the bridgeable divide between “faith talk” and “public health talk. ” Chapter two by Brian Wilcox Sharon Scales Rostosky and colleagues provides a comprehensive research review of the role of religiosity in teen sexual behavior. In addition the National Campaign has prepared a companion pamphlet Snapshots from the Front Line III: Lessons from Faith-Based Efforts to Prevent Teen Pregnancy which highlights the promising work of a handful of innovative faith-based programs around the country. We hope this report helps faith leaders and communities to celebrate the good work they are already doing to prevent teen pregnancy and encourages them to do even more convinces traditional players in the field of teen pregnancy prevention that religious leaders and institutions are allies not adversaries and educates policymakers about the potential faith-based efforts have for helping young people. (excerpt)
RESTORATION OF CELL-MEDIATED IMMUNITY TO ANIMALS BLOCKED BY A HUMORAL RESPONSE

The T cells which mediate delayed-type hypersensitivity (DTH) to sheep red blood cells (SRBC) are blocked by a normal humoral response and cannot be made to function by further immunization. They can be rescued to some extent by treatment with immunopotentiating agents such as cyclophosphamide (CY) which suppresses the antibody response selectively, or by BCG which interferes with the action of serum blocking factors. These two agents together can restore cell-mediated immunity completely, but a further antigenic stimulus is needed to reestablish DTH in mice blocked by a long period of continuous exposure to SRBC.
Emerging Object Representations in the Visual System Predict Reaction Times for Categorization

Recognizing an object takes just a fraction of a second, less than the blink of an eye. Applying multivariate pattern analysis, or “brain decoding”, methods to magnetoencephalography (MEG) data has allowed researchers to characterize, in high temporal resolution, the emerging representation of object categories that underlie our capacity for rapid recognition. Shortly after stimulus onset, object exemplars cluster by category in a high-dimensional activation space in the brain. In this emerging activation space, the decodability of exemplar category varies over time, reflecting the brain’s transformation of visual inputs into coherent category representations. How do these emerging representations relate to categorization behavior? Recently it has been proposed that the distance of an exemplar representation from a categorical boundary in an activation space is critical for perceptual decision-making, and that reaction times should therefore correlate with distance from the boundary. The predictions of this distance hypothesis have been born out in human inferior temporal cortex (IT), an area of the brain crucial for the representation of object categories. When viewed in the context of a time varying neural signal, the optimal time to “read out” category information is when category representations in the brain are most decodable. Here, we show that the distance from a decision boundary through activation space, as measured using MEG decoding methods, correlates with reaction times for visual categorization during the period of peak decodability. Our results suggest that the brain begins to read out information about exemplar category at the optimal time for use in choice behaviour, and support the hypothesis that the structure of the representation for objects in the visual system is partially constitutive of the decision process in recognition.