Miracle-dz/UniFAR-FTUs huggingface dataset:
Resilience From a Virtue Perspective

Resilience refers to one’s ability to adapt and navigate through challenges in the face of a chronic illness and disability (CID). Originally reported in posttraumatic stress literature, resilience has been studied in various contexts; however, very few studies have been conducted from a virtue perspective. Virtue, in psychological terms, is consistent, everyday action toward a worthwhile pursuit, based on one’s values. From a virtue perspective, resilience is defined as a positive by-product of having endured adversities while transforming them into insightful opportunities for renewal. This article introduces the virtue-based psychosocial adaptation model (V-PAM) and its applicability to the study of resilience. College students with CID (N = 256) were recruited, and their resilience was empirically examined based on the proposed model.
FLOW: A First-Language-Oriented Writing Assistant System

Writing in English might be one of the most difficult tasks for EFL (English as a Foreign Language) learners. This paper presents FLOW, a writing assistance system. It is built based on first-language-oriented input function and context sensitive approach, aiming at providing immediate and appropriate suggestions including translations, paraphrases, and n-grams during composing and revising processes. FLOW is expected to help EFL writers achieve their writing flow without being interrupted by their insufficient lexical knowledge.
The crystal structure of the TetR family transcriptional repressor SimR bound to DNA and the role of a flexible N-terminal extension in minor groove binding

SimR, a TetR-family transcriptional regulator (TFR), controls the export of simocyclinone, a potent DNA gyrase inhibitor made by Streptomyces antibioticus. Simocyclinone is exported by a specific efflux pump, SimX and the transcription of simX is repressed by SimR, which binds to two operators in the simR-simX intergenic region. The DNA-binding domain of SimR has a classical helix-turn-helix motif, but it also carries an arginine-rich N-terminal extension. Previous structural studies showed that the N-terminal extension is disordered in the absence of DNA. Here, we show that the N-terminal extension is sensitive to protease cleavage, but becomes protease resistant upon binding DNA. We demonstrate by deletion analysis that the extension contributes to DNA binding, and describe the crystal structure of SimR bound to its operator sequence, revealing that the N-terminal extension binds in the minor groove. In addition, SimR makes a number of sequence-specific contacts to the major groove via its helix-turn-helix motif. Bioinformatic analysis shows that an N-terminal extension rich in positively charged residues is a feature of the majority of TFRs. Comparison of the SimR–DNA and SimR–simocyclinone complexes reveals that the conformational changes associated with ligand-mediated derepression result primarily from rigid-body rotation of the subunits about the dimer interface.
On the micro-architectural impact of clock distribution using multiple PLLs

Clock distribution has traditionally been a circuit design problem with negligible micro-architectural impact. However, for clock distribution networks using multiple phase-locked loops (PLLs), this will most likely not be the case. This paper discusses the micro-architectural impact of using multiple PLLs for clock distribution. Two PLL phase synchronization algorithms are presented and analyzed. They are compared in terms of efficiency, performance, and complexity. For both, the micro-architectural impact is small, but certainly not negligible.
THE DISTRIBUTION OF TEMPERATURE CHARACTERISTICS FOR BIOLOGICAL PROCESSES; CRITICAL INCREMENTS FOR HEART RATES

Disregarding sources of variation known to be present in the unselected data, it is shown that the frequency distribution of temperature characteristics (critical increments) calculated from all known series of observations, pertaining to a great variety of vital processes, exhibits a number of discrete modes. This leads to the view, already derived from evidence of a more specific sort, that such critical increments may be used to characterize definite processes in a controlling system of reactions which seems to be of widespread occurrence in living matter.
Global Gene Expression Profiling through the Complete Life Cycle of Trypanosoma vivax

The parasitic flagellate Trypanosoma vivax is a cause of animal trypanosomiasis across Africa and South America. The parasite has a digenetic life cycle, passing between mammalian hosts and insect vectors, and a series of developmental forms adapted to each life cycle stage. Each point in the life cycle presents radically different challenges to parasite metabolism and physiology and distinct host interactions requiring remodeling of the parasite cell surface. Transcriptomic and proteomic studies of the related parasites T. brucei and T. congolense have shown how gene expression is regulated during their development. New methods for in vitro culture of the T. vivax insect stages have allowed us to describe global gene expression throughout the complete T. vivax life cycle for the first time. We combined transcriptomic and proteomic analysis of each life stage using RNA-seq and mass spectrometry respectively, to identify genes with patterns of preferential transcription or expression. While T. vivax conforms to a pattern of highly conserved gene expression found in other African trypanosomes, (e.g. developmental regulation of energy metabolism, restricted expression of a dominant variant antigen, and expression of ‘Fam50’ proteins in the insect mouthparts), we identified significant differences in gene expression affecting metabolism in the fly and a suite of T. vivax-specific genes with predicted cell-surface expression that are preferentially expressed in the mammal (‘Fam29, 30, 42’) or the vector (‘Fam34, 35, 43’). T. vivax differs significantly from other African trypanosomes in the developmentally-regulated proteins likely to be expressed on its cell surface and thus, in the structure of the host-parasite interface. These unique features may yet explain the species differences in life cycle and could, in the form of bloodstream-stage proteins that do not undergo antigenic variation, provide targets for therapy.
Limits of achievable performance of controlled combustion processes

This paper presents a fundamental limitations-based analysis to quantify limits on obtainable performance for active control of combustion (thermoacoustic) instability. Experimental data from combustor rigs and physics-based models are used to motivate the relevance of both the linear and nonlinear thermoacoustic models. For linear models, Bode integral-based analysis is used to explain peak-splitting observed in experiments. It is shown that large delay in the feedback loop and limited actuator bandwidth are the primary factors that limits the effectiveness of the active control. Explicit bounds on obtainable performance in the presence of delay, unstable dynamics, and limited controller bandwidth are obtained. A multi-input describing function framework is proposed to extend this analysis to the study of nonlinear models that also incorporate the effects of noise. The fundamental limitations are interpreted for a modified sensitivity function defined with respect to noise balance. The framework is applied to the analysis of linear thermoacoustic models with nonlinear on-off actuators and Gaussian noise. The results of the analysis are well-supported by experiments and model simulations. In particular, we reproduce in model simulations and explain analytically the peak-splitting phenomenon observed in experiments
Measures for Unsupervised Fuzzy-Rough Feature Selection

For supervised learning, feature selection algorithms attempt to maximise a given function of predictive accuracy. This function usually considers the ability of feature vectors to reflect decision class labels. It is therefore intuitive to retain only those features that are related to or lead to these decision classes. However, in unsupervised learning, decision class labels are not provided, which poses questions such as; which features should be retained? and, why not use all of the information? The problem is that not all features are important. Some of the features may be redundant, and others may be irrelevant and noisy. In this paper, some new fuzzy-rough set-based approaches to unsupervised feature selection are proposed. These approaches require no thresholding or domain information, and result in a significant reduction in dimensionality whilst retaining the semantics of the data.
A Global Dictionary Based Approach to Fast Similar Text Search in Document Repository

Text plagiarism is growing rapidly with the development of Internet, so many plagiarism detection algorithms have been proposed. However, most algorithms focus on the optimized one-to-one comparison, rather than massive document comparison. The latter algorithms have a limitation in time performance when users conduct an exhaustive search on a huge set of documents. In this paper, we propose an optimized preprocessing model to detect similar text in massive document repositories. This model uses an efficient data structure called GDIC (Global Dictionary) for preprocessing. After filtering stop words, we choose pairs of documents to be inspected using two methods at the same time, both of which use the concept of a common non-stop word to choose pairs of documents to be inspected, each of which uses it in a slightly different way. The first method chooses pairs of documents with a high frequency of common non-stop words in documents in each of these pairs, while the second method chooses pairs with a high proportion of common non-stop words. We experimentally prove the performance of the model. Our experiments with the proposed preprocessing model is drastically reduced searching time to 64~87%, while the sensitivity stands at 77~96%. When we use this model, GDIC generation time accounts for a large proportion of all of the detection time. In future work, we will optimize GDIC creation time to improve the performance of the entire system.
Group therapies for schizophrenia: initial group climate predicts changes in negative symptoms

Abstract Objectives: Whilst group therapies for schizophrenia are effective in reducing negative symptoms, little is known about the mechanisms that bring about these improvements. This study examined the association between group climate (perceived group environment) and negative symptom outcomes. Method: Group climate was rated from eleven video-recorded outpatient therapy groups (N = 85), by three independent researchers blind to clinical outcomes. Multi-level linear modelling was used to explore the relationship between changes in negative symptom scores before and after group therapy, and group climate assessed at the first, second, third and fourth therapy stage. Results: High group engagement, low conflict and low avoidance in the first therapy stage were associated with improvements in negative symptoms. Associations in the latter stages mainly failed to reach statistical significance. Conclusion: Findings highlight the importance of initial group mechanisms in treating negative symptoms. Future research should explore how to promote optimal initial group conditions in the treatment of schizophrenia.
Quasi-Self-Similar Evolution of the Two-Point Correlation Function: Strongly Nonlinear Regime in Ω0 < 1 Universes

The well-known self-similar solution for the two-point correlation function of the density field is valid only in an Einstein-de Sitter universe. We attempt to extend the solution for non-Einstein-de Sitter universes. For this purpose we introduce an idea of quasi-self-similar evolution; this approach is based on the assumption that the evolution of the two-point correlation is a succession of stages of evolution, each of which spans a short enough period to be considered approximately self-similar. In addition, we assume that clustering is stable on scales where a physically motivated "virialization condition" is satisfied. These assumptions lead to a definite prediction for the behavior of the two-point correlation function in the strongly nonlinear regime. We show that the prediction agrees well with N-body simulations in non-Einstein-de Sitter cases and discuss some remaining problems.
Female Employment and Fertility - The Effects of Rising Female Wages

Falling fertility rates have often been linked to rising female wages. However, over the last 30 years the US total fertility rate has been stable while female wages have continued to grow. Over the same period, women's hours spent on housework have declined, but men's have increased. I propose a model with a shrinking gender wage gap that captures these trends. While rising relative wages increase women's labor supply, they also lead to a reallocation of home production from women to men, and a higher use of labor-saving inputs. Both are important in understanding why fertility did not decline further.
An ontological ubiquitous city information platform provided with Cyber-Physical-Social-Systems

Aim of the paper is to illustrate the methodology used to implement an ubiquitous city platform called Wi-City-Plus provided with mobile and centralized Decision Support Systems (DSSs) taking advantage of all the data of city interest, including social data, and those sensed by networked ambient Cyber-Physical-Systems (CPSs). The paper proposes to model such data by a suitable RDF ontology specifically designed to support the main user scenarios in smart cities and to implement both the technical and the service interoperability envisaged by the CPS ideal model. This ontology is obtained by reusing existing ontologies and allows the DSSs to access by SPARQL queries all the relevant data independent of the proprietary technology of the CPSs and of the DBMS adopted by the data owners. Examples derived from the pilot version of the proposed platform allows us to point out advantages and perspectives of our approach.
Modeling Diachronic Change in Scientific Writing with Information Density

Previous linguistic research on scientific writing has shown that language use in the scientific domain varies considerably in register and style over time. In this paper we investigate the introduction of information theory inspired features to study long term diachronic change on three levels: lexis, part-of-speech and syntax. Our approach is based on distinguishing between sentences from 19th and 20th century scientific abstracts using supervised classification models. To the best of our knowledge, the introduction of information theoretic features to this task is novel. We show that these features outperform more traditional features, such as token or character n-grams, while leading to more compact models. We present a detailed analysis of feature informativeness in order to gain a better understanding of diachronic change on different linguistic levels.
Software ecosystems: a software ecosystem strategy assessment model

Software companies and organizations increasingly open up their business to other software companies and as a consequence they find themselves in an ecosystem of software companies, developers and partners. These actors, and especially the software organization that is at the core of this ecosystem, are having difficulties in getting insight into this ecosystem and how their actions influence its performance. In this paper a model is presented that describes the key characteristics of a Software Ecosystem. The central hub of a Software Ecosystem can use data on these characteristics to its strategic advantage. This is demonstrated by the use of the model in a case study of a hub in a software ecosystem.
A generalized capacitors voltage estimation scheme for multilevel converters

In this paper a generalized scheme for capacitors voltage estimation for multilevel converters based on the converter switching states and using only a single voltage sensor at the AC side of the converter is proposed. The estimation of the capacitors voltage is performed after a capacitor is switched on or off and hence the estimation frequency is dependent on the converter switching frequency. In between the switching periods, the scheme can keep track of the capacitors voltage by monitoring the current flowing through the capacitor until the next transition. The effectiveness of the proposed scheme is demonstrated on a single-phase 7-level cascaded H-Bridge multilevel converter based STATCOM by simulation in the Matlab/Simulink environment.
The Developmental Effect of State Alcohol Prohibitions at the Turn of the Twentieth Century

We examine the quasi‐randomization of alcohol consumption created by state‐level alcohol prohibition laws passed in the United States in the early part of the twentieth century. Using a large dataset of World War II enlistees, we exploit the differential timing of these laws to examine their effects on adult educational attainment, obesity, and height. We find statistically significant effects for education and obesity that do not appear to be the result of pre‐existing trends. Our findings add to the growing body of economic studies that examine the long‐run impacts of in utero and childhood environmental conditions.
Geometrical and statistical properties of vision models obtained via maximum differentiation

We examine properties of perceptual image distortion models, computed as the mean squared error in the response of a 2-stage cascaded image transformation. Each stage in the cascade is composed of a linear transformation, followed by a local nonlinear normalization operation. We consider two such models. For the first, the structure of the linear transformations is chosen according to perceptual criteria: a center-surround filter that extracts local contrast, and a filter designed to select visually relevant contrast according to the Standard Spatial Observer. For the second, the linear transformations are chosen based on statistical criterion, so as to eliminate correlations estimated from responses to a set of natural images. For both models, the parameters that govern the scale of the linear filters and the properties of the nonlinear normalization operation, are chosen to achieve minimal/maximal subjective discriminability of pairs of images that have been optimized to minimize/maximize the model, respectively (we refer to this as MAximum Differentiation, or “MAD”, Optimization). We find that both representations substantially reduce redundancy (mutual information), with a larger reduction occurring in the second (statistically optimized) model. We also find that both models are highly correlated with subjective scores from the TID2008 database, with slightly better performance seen in the first (perceptually chosen) model. Finally, we use a foveated version of the perceptual model to synthesize visual metamers. Specifically, we generate an example of a distorted image that is optimized so as to minimize the perceptual error over receptive fields that scale with eccentricity, demonstrating that the errors are barely visible despite a substantial MSE relative to the original image.
Voluntary Modulation of Anterior Cingulate Response to Negative Feedback

Anterior cingulate and medial frontal cortex (dACC/mFC) response to negative feedback represents the actions of a generalized error-monitoring system critical for the management of goal-directed behavior. Magnitude of dACC/mFC response to negative feedback correlates with levels of post-feedback behavioral change, and with proficiency of operant learning processes. With this in mind, it follows that an ability to alter dACC/mFC response to negative feedback may lead to representative changes in operant learning proficiency. To this end, the present study investigated the extent to which healthy individuals would show modulation of their dACC/mFC response when instructed to try to either maximize or minimize their neural response to the presentation of contingent negative feedback. Participants performed multiple runs of a standard time-estimation task, during which they received feedback regarding their ability to accurately estimate a one-second duration. On Watch runs, participants were simply instructed to try to estimate as closely as possible the one second duration. On Increase and Decrease runs, participants performed the same task, but were instructed to “try to increase [decrease] their brain's response every time they received negative feedback”. Results indicated that participants showed changes in dACC/mFC response under these differing instructional conditions: dACC/mFC activity following negative feedback was higher in the Increase condition, and dACC activity trended lower in the Decrease condition, compared to the Watch condition. Moreover, dACC activity correlated with post-feedback performance adjustments, and these adjustments were highest in the Increase condition. Potential implications for neuromodulation and facilitated learning are discussed.
SciNet: a system for browsing scientific literature through keyword manipulation

Techniques for both exploratory and known item search tend to direct only to more specific subtopics or individual documents, as opposed to allowing directing the exploration of the information space. We present SciNet, an interactive information retrieval system that combines Reinforcement Learning techniques along with a novel user interface design to allow active engagement of users in directing the search. Users can directly manipulate document features (keywords) to indicate their interests and Reinforcement Learning is used to model the user by allowing the system to trade off between exploration and exploitation. This gives users the opportunity to more effectively direct their search.
Photon pair generation 1310 – 1550 nm based on active photonic crystal: Heralded single photon source model

We report a heralded single photon source model; it consists on active two-dimensional crystal over LiNbO3 excited by a laser source at (1310 nm and 1550 nm). Photons detected through the crystal are filtered to eliminate of the laser source one. The photons at 1310 nm and 1550 nm will be spatially separated; the photon at 1310 nm is the trigger to announce the photon at 1550 nm. The contribution of this work is the using of the 2D photonic crystal to improve the conversion light efficiency and obtain height DOM at 1310 nm and 1550 nm, which will increase the probability to obtain single photon. This study can serve for the conception of new quantum communications protocols.
Context Aware Routing of Enterprise User Communications

This paper develops a context aware framework to address the diverse communication needs of a modern enterprise. Such enterprises are characterized by workers in different locations, subject to different policies, using different communication devices, and having varying degrees of skill sets. This diversity poses challenges in finding the most effective human worker (agent) for tasks like fielding a customer request, helping another agent with additional expert knowledge, or more generally help complete a task like a supply chain exception. We focus on the problem of routing communications to the most effective agent using a spectrum of contextual knowledge: availability, media type, activity, expertise, and location. We determine an optimal 'request-to-agent' routing based on several metrics of effectiveness depending on the communication context. The optimal agent is selected to communicate on a specific media who minimizes the expected duration of interaction while maximizes the probability of successful call completion. Based on our model we have conducted simulations involving context aware and non-context aware routing scenarios. The results indicate that the context aware routing outperforms other conventional request-routing techniques. The work presented here can impact routing algorithms, as well as address problems related to enterprise staffing and temporal variation of context for the agent