Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Decoherence of Primordial Fluctuations During Inflation

: We study the process whereby quantum cosmological perturbations become classical within inﬂationary cosmology. By setting up a master-equation formulation we show how quantum coherence for super-Hubble modes can be destroyed by its coupling to the environment provided by sub-Hubble modes. We identify what features the sub-Hubble environment must have in order to decohere the longer wavelengths, and identify how the onset of decoherence (and how long it takes) depends on the properties of the sub-Hubble physics which forms the environment. Our results show that the decoherence process is largely insensitive to the details of the coupling between the sub- and super-Hubble scales. They also show how locality implies , quite generally, that the decohered density matrix at late times is diagonal in the ﬁeld representation (as is implicitly assumed by extant calculations of inﬂationary density perturbations). Our calculations also imply that decoherence can arise even for couplings which are as weak as gravitational in strength.
Incidental Learning and Long‐Term Retention of New Word Meanings From Stories: The Effect of Number of Exposures

This study used a Web‐based naturalistic story‐reading paradigm to investigate the impact of number of exposures on incidental acquisition and long‐term retention of new meanings for known words by native English‐speaking adults. Participants read one of four custom written stories in which they encountered novel meanings (e.g., a safe concealed within a piece of furniture) for familiar words (foam). These meanings appeared two, four, six, or eight times in the narrative. Results showed reasonably good memory of the new meanings, assessed by cued recall of novel meanings and word forms, after only two exposures, emphasizing the importance of initial encounters. Accuracy in cued recall of novel meanings showed a linear, incremental increase with more exposures. There was no significant forgetting after 1 week, regardless of the number of exposures during training, demonstrating the efficiency with which adults acquire new word meanings incidentally through reading and retain them over time.
Flexible wound assessment system for diabetic patient using android smartphone

Diabetic ulcers represent a significant health issue. Currently, clinicians and nurses mainly base their wound assessment on visual examination of wound size and healing status, while the patients themselves seldom have an opportunity to play an active role. Hence, a more quantitative and cost-effective examination method that enables the patients and their caregivers to take a more active role in daily wound care potentially can accelerate wound healing, save travel cost and reduce healthcare expenses. Considering the prevalence of smart-phones with a high-resolution digital camera, assessing wounds by analysing images of chronic ulcers is an attractive option. We propose a novel wound image analysis system implemented solely on the Android smart-phone. The wound image is captured by the camera on the smart-phone with the assistance of an android App. After that, the smart-phone performs wound segmentation by applying the accelerated mean-shift algorithm. Specifically, the outline of the affected body parts is determined based on skin color, and the wound boundary is found using a simple connected region detection method. Within the wound boundary, the healing status is next assessed based on Red-Yellow-Black color evaluation model. Moreover, the healing status is quantitatively assessed, based on trend analysis of time records for a given patient.
The legacy and trajectories of multicultural education: recognition, refusal, and movement building in troubling times

ABSTRACT In this conceptual analysis paper, I provide a brief overview of multicultural education and highlight the critiques to the field that demonstrate challenges to implementing an education that is multicultural especially when considering students from minoritized and global majority backgrounds. Next, I explore the changing political and social context that suggests, among other things, the need for re/newed orientations to multicultural education. I focus on two important scholarly lines of inquiry – Decolonization and Antiblack theorizing – that might inform possible trajectories for multicultural education including a pedagogy of recognition and a pedagogy of refusal. I conclude by identifying questions that lay at the intersection of these two pedagogies and share some final thoughts about the role of social movements in forging a more critical, responsive approach to multicultural education.
Characterization of Intonation in Karṇāṭaka Music by Parametrizing Context-based Svara Distributions

Intonation is a fundamental music concept that has a special relevance in Indian art music. It is characteristic of the rāga and intrinsic to the musical expression of the performer. Describing intonation is of importance to several information retrieval tasks like the development of rāga and artist similarity measures. In our previous work, we proposed a compact representation of intonation based on the parametrization of the pitch histogram of a performance and demonstrated the usefulness of this representation through an explorative rāga recognition task in which we classified 42 vocal performances belonging to 3 rāgas using parameters of a single svara. In this paper, we extend this representation to employ context-based svara distributions, which are obtained with a different approach to find the pitches belonging to each svara. We quantitatively compare this method to our previous one, discuss the advantages, and the necessary melodic analysis to be carried out in future.
Differences according to Sex in Sociosexuality and Infidelity after Traumatic Brain Injury

Objective. To explore differences according to sex in sociosexuality and infidelity in individuals with TBI and in healthy controls. Participants. Forty-two individuals with mild, moderate, and severe TBI having completed a postacute TBI rehabilitation program, at least six months after injury, and 47 healthy controls. Main Measures. Sociosexual Orientation Inventory-Revised (SOI-R) and Attitudes toward Infidelity Scale. Results. Overall, men score significantly higher than women in sociosexuality. However, there was a nonsignificant trend towards a reduction of sociosexuality levels in men with TBI. Infidelity levels were comparable in healthy controls and individuals with TBI. In individuals with TBI, less acceptance of infidelity was significantly associated with an unrestricted sociosexual orientation, but not in healthy controls. Conclusions. As documented in previous cross-cultural studies, men have higher levels of sociosexuality than women. However, men with TBI showed a tendency towards the reduction of sociosexuality. The possibility of a latent explanatory variable is suggested (e.g., post-TBI neuroendocrinological changes). TBI does not seem to have an impact on infidelity, but individuals with TBI who express less acceptance of infidelity also report a more promiscuous mating strategy regarding their behavior, attitudes, and desire. Theoretical implications are discussed in terms of evolutionary theories of human sexuality and neuropsychology.
Creation of a highly detailed, dynamic, global model and map of science

The majority of the effort in metrics research has addressed research evaluation. Far less research has addressed the unique problems of research planning. Models and maps of science that can address the detailed problems associated with research planning are needed. This article reports on the creation of an article‐level model and map of science covering 16 years and nearly 20 million articles using cocitation‐based techniques. The map is then used to define discipline‐like structures consisting of natural groupings of articles and clusters of articles. This combination of detail and high‐level structure can be used to address planning‐related problems such as identification of emerging topics and the identification of which areas of science and technology are innovative and which are simply persisting. In addition to presenting the model and map, several process improvements that result in greater accuracy structures are detailed, including a bibliographic coupling approach for assigning current papers to cocitation clusters and a sequential hybrid approach to producing visual maps from models.
Light-cone superstring field theory, pp-wave background and integrability properties

We show that the three-string vertex coefficients in light-cone open string field theory satisfy the Hirota equations for the dispersionless Toda lattice hierarchy. We show that Hirota equations allow us to calculate the correlators of an associated quantum system where the Neumann coefficients represent the two-point functions. We consider next the three-string vertex coefficients of the light-cone string field theory on a maximally supersymmetric pp-wave background. Using the previous results, we are able to show that these Neumann coefficients satisfy the Hirota equations for the full Toda lattice hierarchy at least up to second order in the ‘string mass’ μ.
Analysis Methodology of Inelastic Constitutive Parameter Using State Space Method and Neural Network

Background/Objectives: In this paper, we present a method for describing a set of variables of an inelastic constitutive equation based on state space method (SSM) and neural network (NN). The advantage of this method is that it can identify the appropriate parameters.Methods/Statistical analysis: Two NNs based on SSM are proposed. One outputs the ratio of inelastic strain for the internal parameters of the material, and the other is the following state of the inelastic strain ratio and material internal variable. Both NNs were trained and successfully collected using input and output data generated by Chaboche 's model.Findings: As a result, previous NNs have demonstrated their validity as a powerful material model. However, the training data for the proposed NN can’t be easily obtained from actual experimental data. Previous neural networks can reproduce the original stress-strain curves. The NNs also produced untrained curves to demonstrate interpolation capabilities. It was also found that the NNs can be estimated to be close to training data. The author defines the implicit constitutive model and proposes the implicit viscous constitutive model using NNs. In modeling, inelastic behavior is generalized in state space representation, and the state space form is constructed by NNs using an input-output data sets. The proposed model was first created from the pseudo-experimental data generated by one of the commonly used configuration models and has been found to be a good replacement for the model. The actual experimental data was then tested, and the proposed model showed the accuracy of its superiority over all existing specified models because the amount of model errors was negligible.Improvements/Applications: The comparison between the NN constitutive laws with the Chaboche’s model indicates that the NN constitutive law generated curves with less model errors than the experimental data, thereby indicating the superiority of the neural constitutive law to explicit constitutive laws as a material model.
The SCUBA Legacy Catalogues: Submillimeter-Continuum Objects Detected by SCUBA

We present the SCUBA Legacy Catalogues, two comprehensive sets of continuum maps (and catalogs) using data at 850 and 450 μm of the various astronomical objects obtained with the Submillimetre Common User Bolometer Array (SCUBA). The Fundamental Map Data Set contains data only where superior atmospheric opacity calibration data were available. The Extended Map Data Set contains data regardless of the quality of the opacity calibration. Each data set contains 1.2° × 1.2° maps at locations where data existed in the JCMT archive, imaged using the matrix inversion method. The Fundamental Data Set is composed of 1423 maps at 850 μm and 1357 maps at 450 μm. The Extended Data Set is composed of 1547 maps at 850 μm. Neither data set includes high sensitivity, single-chop SCUBA maps of “cosmological fields” nor solar system objects. Each data set was used to determine a respective object catalogue, consisting of objects identified within the respective 850 μm maps using an automated identification algorithm. The Fundamental and Extended Map Object Catalogues contain 5061 and 6118 objects, respectively. Objects are named based on their respective J2000.0 position of peak 850 μm intensity. The catalogues provide for each object the respective maximum 850 μm intensity, estimates of total 850 μm flux and size, and tentative identifications from the SIMBAD Database. Where possible, the catalogues also provide for each object its maximum 450 μm intensity and total 450 μm flux and flux ratios.
Public Transit Bus Procurement: the Role of Energy Prices, Regulation and Federal Subsidies

The U.S. public transit system represents a multi-billion dollar industry that provides essential transit services to millions of urban residents. We study the market for new transit buses that features a set of non-profit transit agencies purchasing buses primarily from a few domestic bus makers. In contrast with private passenger vehicles, the fuel economy of public buses has not improved during the last thirty years and is irresponsive to fuel price changes. To understand these findings, we build a model of bus fleet management decisions of public transit agencies that yields testable hypotheses. Our empirical analysis of bus fleet turnover and capital investment highlights the role of energy prices, environmental regulations, and the “Buy America” mandate associated with receiving federal funding to purchase public transit buses.
Synthetic properties of bright metal-poor variables I. "Anomalous" Cepheids

We present new grids of evolutionary models for the so-called "Anomalous" Cepheids (ACs), adopting Z = 0.0001 and various assumptions on the progenitor mass and mass-loss efficiency. These computations are combined with the results of our previous set of pulsation models and used to.build synthetic populations of the predicted pulsators as well as to provide a Mass-Luminosity relation in the absence of mass-loss. We investigate the effect of mass-loss on the predicted boundaries of the instability strip and we find that the only significant dependence occurs in the Period-Magnitude plane, where the synthetic distribution of the pulsators is, on average, brighter by about 0.1 mag than the one in absence of mass-loss. Tight Period-Magnitude relations are derived in the K hand for both fundamental and first overtone pulsators, providing a useful tool for distance evaluations with an intrinsic uncertainty of about 0.15 mag, which decreases to ∼0.04 mag if the mass term is taken into account. The constraints provided by the evolutionary models are used to derive evolutionary (i.e., mass-independent) Period-Magnitude-Color relations which provide distance determinations with a formal uncertainty of the order of ∼0.1 mag, once the intrinsic colors are well known. We also use model computations from the literature to investigate the effect of metal content both on the instability strip and on the evolutionary Period-Magnitude-Color relations. Finally, we compare our theoretical predictions with observed variables and we confirm that a secure identification of actual ACs requires simultaneous information on period, magnitude and color, that also provide constraints on the pulsation mode.
Forecasting Stock Price Using a Genetic Fuzzy Neural Network

The use of neural networks (NNs) for stock market forecast is quite common because of their excellent performances of treating non-linear data with self-learning capability. However, neural networks suffer from the difficulty to deal with qualitative information and the "black box" syndrome that more or less limited their applications in practice. The fuzzy neural networks (FNN) allow to add rules to neural networks. This avoids the "black-box" but lacks of effective learning capability. To overcome these drawbacks, in this study an integration of genetic algorithm and fuzzy neural networks (GFNN) are proposed to forecast stock price. The results indicate that the predictive accuracies obtained from GFNN are much higher than the ones obtained from NNs. To make this clearer, an illustrative example is also demonstrated in this study.
Evaluating Potential Improvements of Collaborative Filtering with Opinion Mining

An integration of an Opinion Mining approach with a Collaborative Filtering algorithm has been applied to the Yelp dataset to improve the predictions through the information provided by the user-generated textual reviews. The research, still in progress, based the Opinion Mining approach on the syntactic analysis of textual reviews and on a beginning polarity evaluation of the sentences. The predictions produced in this way was blended with the predictions coming from a Biased Matrix Factorization algorithm obtaining interesting results in terms of Root Mean Squared Error (RMSE), with potential enhancements. We intend to improve these results in a further phase of activity by including in the Opinion Mining approach the semantic disambiguation and by using better criteria of evaluation of the reviews taking into account a set of 12 business aspects. The Opinion Mining approach will be evaluated comparing the output in terms of predictions with the values manually assigned by a small group of people to a sample of the same reviews.
Graph-based Exploration of Non-graph Datasets

Graphs or networks provide a powerful abstraction to view and analyze relationships among different entities present in a dataset. However, much of the data of interest to analysts and data scientists resides in non-graph forms such as relational databases, JSON, XML, CSV and text. The effort and skill required in identifying and extracting the relevant graph representation from data is often the prohibitive and limits a wider adoption of graph-based analysis of non-graph data. In this paper, we demonstrate our system called GraphViewer, for accelerated graph-based exploration and analysis. It automatically discovers relevant graphs implicit within a given non-graph dataset using a set of novel rule-based and data-driven techniques, and optimizes their extraction and storage. It computes several node and graph level metrics and detects anomalous entities in data. Finally, it summarizes the results to support interpretation by a human analyst. While the system automates the computationally intensive aspects of the process, it is engineered to leverage human domain expertise and instincts to fine tune the data exploration process.
Identification and Fuzzy Controller Design for Nonlinear Uncertain Systems with Input Time-Delay

This paper considers the identification and fuzzy controller design for nonlinear uncertain systems in presence of unknown input time-delay. Firstly, a time-delay Takagi-Sugeno-Kang (TSK) type fuzzy neural system (TDFN) is proposed to identify a class of nonlinear input time-delay systems. The input- output signals of nonlinear systems are used to identify the system dynamics and unknown time-delay, and then construct the system in the form TSK-fuzzy time-delay model. Each fuzzy rule has a corresponding linear system with an input time-delay as the consequent part. Based on parallel distribution compensation (PDC) approach, the Smith predictor compensation and dominate pole assignment technique are then adopted to design the fuzzy PID controller. Several simulations are shown to demonstrate the effectiveness and control performance of the proposed approach.
Design exploration assessment methodology: testing the guidance of design processes

This paper introduces the design exploration assessment methodology (DEAM) for comparing design process impact and outcome. Current practice fails to reliably generate high-performing alternatives in part because it lacks systematic means to compare existing or emerging design processes. Researchers lack empirical methods and data to evaluate design challenges and the strategies available to address them. In this paper, we document and then apply the DEAM to the professional implementation of six design strategies across two design challenges using the charrette test method. The results are used to compare the strategies according to the performance of the solution(s) generated. For the strategies and challenges investigated, more information during design does not always assist the designer to produce better performing alternatives. We discuss possible explanations and conclude with a discussion of the strengths and weaknesses of the DEAM as an evaluation method. Initial findings demonstrate that the DEAM is a method capable of providing a meaningful comparison of strategies in the domain of energy-efficient design challenges.
The Research of Distributed Intrusion Detection and Communication Protocol Based on Entity

The existed distributed intrusion detection system adopt the architecture which the data is collected and analyzed centrally, in which there are some defects. The defects are like simple point invalidation and bad extension. On the base of analyzing existed distributed intrusion detection system , this paper designs one distributed intrusion detection system based on the entity model and proposed a secure communication protocol. In order to guarantee security, reliability and integrity among entities in the model, this paper has designed an object- oriented exchange model of information. The paper also has designed a safe transport protocol based on ECC, DES symmetrical and the asymmetrical encryption technology, so it can assure to communicates safely and effectively with each kind of entity.
Cost-effectiveness of interventions to return employees to work following long-term sickness absence due to musculoskeletal disorders.

BACKGROUND Sickness absence costs the UK economy around £20 billion per year. This study aims to assess the cost-effectiveness of interventions to return employees with musculoskeletal disorders to work, one of the major causes of long-term sickness absence, using a mathematical model. METHODS A Markov model was developed to assess the cost-effectiveness of three interventions: a workplace intervention; a physical activity and education intervention and a physical activity, education and workplace visit intervention. Extensive sensitivity analyses were undertaken to assess the impact of uncertainties upon the model results. RESULTS All interventions assessed are estimated to have a cost per quality-adjusted life year gained below £3000 compared with usual care within the UK from a National Health Service (NHS) or societal perspective. Moreover, any intervention which returns at least an additional 3% of employees to work and costs less than an additional £3000 per employee, is likely to be considered economically attractive compared with usual care, relative to other interventions routinely funded by the NHS. CONCLUSIONS This is the first economic evaluation in this area which extrapolates data beyond trial follow-up and synthesizes evidence from numerous sources. This sort of modelling approach should be considered for informing other public health policy decisions.