Miracle-dz/UniFAR-FTUs huggingface dataset:
Gestational weight gain in women with systemic lupus erythematosus

Objective The objective of this study was to estimate the proportion of pregnant women with systemic lupus erythematosus meeting Institute of Medicine guidelines for gestational weight gain and determine correlates of adherence to guidelines. Methods Singleton, live births in the Hopkins Lupus Pregnancy Cohort 1987–2015 were included. Pre-pregnancy weight was the weight recorded 12 months prior to pregnancy/first trimester. Final weight was the last weight recorded in the third trimester. Adherence to Institute of Medicine guidelines (inadequate, adequate, or excessive) was based on pre-pregnancy body mass index. Fisher’s exact test and analysis of variance determined factors associated with not meeting guidelines. Stepwise selection estimated predictors of gestational weight gain. Results Of the 211 pregnancies, 34%, 24% and 42% had inadequate, adequate and excessive gestational weight gain, respectively. In exploratory analyses, differences in Institute of Medicine adherence were observed by pre-pregnancy body mass index, race, elevated creatinine during pregnancy and pre-pregnancy blood pressure. Odds of inadequate and excessive gestational weight gain increased 12% with each 1 kg/m2 increase in pre-pregnancy body mass index. Lower maternal education was associated with increased odds of inadequate and excessive gestational weight gain. Conclusions As in the general population, most women with systemic lupus erythematosus did not meet Institute of Medicine guidelines. Our results identified predictors of gestational weight gain to aid in targeted interventions to improve guideline adherence in this population.
Automatic Vehicle Registration System for Tollbooths

With the advancement in computation, the era of automatic systems is rapidly taking over. Image processing is an essential tool being widely used in effective solutions concerning such systems. In our paper entitled “Automatic Vehicle Registration System for Tollbooths”, we propose the use of image processing techniques for optimal character recognition in a license plate. We have proposed an effective algorithm to extract the digits from the license plate of a vehicle which can be used to fully automatize the access system at the barrier gate. The algorithm accounts for various anomalies in the system which may include movements at the gates and so on. The system proposed by us for calculation of tariff using the different image processing techniques has also been explained in detail. Its advantages over conventional system include low installation and maintenance cost amongst others.
A luminance-contrast-aware disparity model and applications

Binocular disparity is one of the most important depth cues used by the human visual system. Recently developed stereo-perception models allow us to successfully manipulate disparity in order to improve viewing comfort, depth discrimination as well as stereo content compression and display. Nonetheless, all existing models neglect the substantial influence of luminance on stereo perception. Our work is the first to account for the interplay of luminance contrast (magnitude/frequency) and disparity and our model predicts the human response to complex stereo-luminance images. Besides improving existing disparity-model applications (e.g., difference metrics or compression), our approach offers new possibilities, such as joint luminance contrast and disparity manipulation or the optimization of auto-stereoscopic content. We validate our results in a user study, which also reveals the advantage of considering luminance contrast and its significant impact on disparity manipulation techniques.
Independent association between rate of clearance of infection and clinical outcome of HIV-associated cryptococcal meningitis: analysis of a combined cohort of 262 patients.

BACKGROUND Progress in therapy for cryptococcal meningitis has been slow because of the lack of a suitable marker of treatment response. Previously, we demonstrated the statistical power of a novel endpoint, the rate of clearance of infection, based on serial quantitative cultures of cerebrospinal fluid, to differentiate the fungicidal activity of alternative antifungal drug regimens. We hypothesized that the rate of clearance of infection should also be a clinically meaningful endpoint. METHODS We combined data from cohorts of patients with human immunodeficiency virus-associated cryptococcal meningitis from Thailand, South Africa, and Uganda, for whom the rate of clearance of infection was determined, and clinical and laboratory data prospectively collected, and explored the association between the rate of clearance of infection and mortality by Cox survival analyses. RESULTS The combined cohort comprised 262 subjects. Altered mental status at presentation, a high baseline organism load, and a slow rate of clearance of infection were independently associated with increased mortality at 2 and 10 weeks. Rate of clearance of infection was associated with antifungal drug regimen and baseline cerebrospinal fluid interferon-gamma levels. CONCLUSIONS The results support the use of the rate of clearance of infection or early fungicidal activity as a means to explore antifungal drug dosages and combinations in phase II studies. An increased understanding of how the factors determining outcome interrelate may help clarify opportunities for intervention.
Optimal Cloud Instance Acquisition via IaaS Cloud Brokerage with Volume Discount

Commercial cloud providers, e.g., Amazon EC2, offer the volume discount for large instance reservation in a time slot, and the majority of cloud jobs are delay-tolerant and do not need to be processed intermittently. These two features create an opportunity for the cloud brokerage service which aggregates and schedules cloud users' rental requests to earn volume discounts from cloud providers and sell to cloud users at a cheap price. A challenge for the broker is to properly schedule delay-tolerant jobs in order to maximize the volume discount amount over time. The scheduling idea is to generate several job bundles so each job bundle can get discount. In this paper, we discuss this problem from the homogeneous model first, where each job has the same processing time and delay-tolerant time, and we propose a dynamic programming approach. Then, we extend the model into the heterogeneous model, where the job processing time and the job deadline can be arbitrary values. In the heterogeneous scenario, we prove that the proposed problem is NP-hard even when the job processing time is unit. Then, we propose a greedy approach which turns out to have an approximation of $O(\ln n)$, where $n$ is the total job number. Extensive trace-driven experiments from Google cluster trace demonstrates that our schemes achieve good performances.
Scaling aspects of river flow routing

Scaling aspects of river flow routing are studied by comparing two flow routing schemes, one designed for use in coupled general circulation models (GCMs) and operated at large spatial scales (∼350 km), and the other designed for use in typical hydrological applications at small spatial scales (∼25 km). The same runoff data are used as input into the two routing schemes, and comparisons are made between mean annual, mean monthly and daily streamflow simulated at four locations within the Mackenzie River Basin. The results suggest that for the purpose of realistically modelling monthly streamflow at the mouth of the rivers in GCMs, flow routing at large spatial scales gives similar results. However, the amplitude of the annual streamflow cycle is slightly but characteristically larger, when routing is performed at large spatial scales. Flow routing at large spatial scales also results in overestimation of high flows, while low flows are underestimated. Copyright © 2001 John Wiley & Sons, Ltd.
Severe acute radiation syndrome: treatment of a lethally 60Co-source irradiated accident victim in China with HLA-mismatched peripheral blood stem cell transplantation and mesenchymal stem cells

This is a case report of a 32-year-old man exposed to a total body dose of 14.5 Gy γ-radiation in a lethal 60Co-source irradiation accident in 2008 in China. Frequent nausea, vomiting and marked neutropenia and lymphopenia were observed from 30 min to 45 h after exposure. HLA-mismatched peripheral blood stem cell transplantation combined with infusion of mesenchymal stem cells was used at Day 7. Rapid hematopoietic recovery, stable donor engraftment and healing of radioactive skin ulceration were achieved during Days 18–36. The patient finally developed intestinal obstruction and died of multi-organ failure on Day 62, although intestinal obstruction was successfully released by emergency bowel resection.
Active SAmpling Protocol (ASAP) to Optimize Individual Neurocognitive Hypothesis Testing: A BCI-Inspired Dynamic Experimental Design

The relatively young field of Brain-Computer Interfaces has promoted the use of electrophysiology and neuroimaging in real-time. In the meantime, cognitive neuroscience studies, which make extensive use of functional exploration techniques, have evolved toward model-based experiments and fine hypothesis testing protocols. Although these two developments are mostly unrelated, we argue that, brought together, they may trigger an important shift in the way experimental paradigms are being designed, which should prove fruitful to both endeavors. This change simply consists in using real-time neuroimaging in order to optimize advanced neurocognitive hypothesis testing. We refer to this new approach as the instantiation of an Active SAmpling Protocol (ASAP). As opposed to classical (static) experimental protocols, ASAP implements online model comparison, enabling the optimization of design parameters (e.g., stimuli) during the course of data acquisition. This follows the well-known principle of sequential hypothesis testing. What is radically new, however, is our ability to perform online processing of the huge amount of complex data that brain imaging techniques provide. This is all the more relevant at a time when physiological and psychological processes are beginning to be approached using more realistic, generative models which may be difficult to tease apart empirically. Based upon Bayesian inference, ASAP proposes a generic and principled way to optimize experimental design adaptively. In this perspective paper, we summarize the main steps in ASAP. Using synthetic data we illustrate its superiority in selecting the right perceptual model compared to a classical design. Finally, we briefly discuss its future potential for basic and clinical neuroscience as well as some remaining challenges.
Purchasing Power Parity in Theory and Practice : The Case of Singapore

We provide a practical assessment of the bi-lateral PPP relationship between Singapore and the United States. It is ‘practical’ in the sense that the impact of the asset market, changes in policies and economic fundamentals, and the presence of measurement errors are explicitly considered in the study of the PPP relationship. We hope that this study will put in perspective the theoretical and practical considerations that are often overlooked in the study and test for PPP. Using Johansen's vector error correction model (VECM), we find strong evidence of PPP between the two countries during the period 1981-95. Due to the short time span of the data (15 years), the impact of the asset market has to be included to stabilise the PPP relationship. The Singapore exchange rate is also found to be weakly exogenous to the system which is consistent with the exchange rate management policy of the country.
Stochastic Blockmodeling of the Modules and Core of the Caenorhabditis elegans Connectome

Recently, there has been much interest in the community structure or mesoscale organization of complex networks. This structure is characterised either as a set of sparsely inter-connected modules or as a highly connected core with a sparsely connected periphery. However, it is often difficult to disambiguate these two types of mesoscale structure or, indeed, to summarise the full network in terms of the relationships between its mesoscale constituents. Here, we estimate a community structure with a stochastic blockmodel approach, the Erdős-Rényi Mixture Model, and compare it to the much more widely used deterministic methods, such as the Louvain and Spectral algorithms. We used the Caenorhabditis elegans (C. elegans) nervous system (connectome) as a model system in which biological knowledge about each node or neuron can be used to validate the functional relevance of the communities obtained. The deterministic algorithms derived communities with 4–5 modules, defined by sparse inter-connectivity between all modules. In contrast, the stochastic Erdős-Rényi Mixture Model estimated a community with 9 blocks or groups which comprised a similar set of modules but also included a clearly defined core, made of 2 small groups. We show that the “core-in-modules” decomposition of the worm brain network, estimated by the Erdős-Rényi Mixture Model, is more compatible with prior biological knowledge about the C. elegans nervous system than the purely modular decomposition defined deterministically. We also show that the blockmodel can be used both to generate stochastic realisations (simulations) of the biological connectome, and to compress network into a small number of super-nodes and their connectivity. We expect that the Erdős-Rényi Mixture Model may be useful for investigating the complex community structures in other (nervous) systems.
Activity of descending propriospinal axons in the turtle hindlimb enlargement during two forms of fictive scratching: phase analyses

In the preceding companion article (Berkowitz and Stein, 1994b), we showed that many descending propriospinal neurons in the turtle were rhythmically activated during two different motor patterns, fictive rostral scratching and fictive pocket scratching. In this article, we present phase analyses of the activity of each such neuron during fictive scratching. Each neuron's activity was concentrated in a particular phase of the ipsilateral hip flexor muscle nerve (VP-HP) activity cycle; each had a distinct "preferred phase. " Each neuron's preferred phase during fictive rostral scratching was similar to its preferred phase during fictive pocket scratching. This result is consistent with the idea that some descending propriospinal neurons may contribute to the generation of both rostral scratching and pocket scratching. Many descending propriospinal neurons were rhythmically activated during fictive scratching evoked on either side of the body. This activity may contribute to production of bilateral hindlimb movements during scratching. It is also possible that synaptic interactions between the two sides of the spinal cord may be important in generating the motor patterns for movement of a single hindlimb. In addition, we present a model which illustrates that a population of propriospinal neurons, each of which is broadly tuned to a region of the body surface and is rhythmically activated in a constant phase of the hip control cycle, could mediate the selection and generation of rostral scratching and pocket scratching. Thus, the selection of an appropriate motor pattern and the production of the required knee-hip synergy may each be distributed over a diverse population of spinal cord neurons. This model requires that each such neuron project to both knee muscle and hip muscle motoneurons. According to this model, the process of selecting a motor pattern would not be completed until knee muscle motoneurons integrate overlapping excitatory and inhibitory inputs.
Unary model 1 . 1 Syntax and operational semantics

This note is based on lectures by Lars Birkedal and work written by Aleš Bizjak and Kasper Svendsen among others. In Section 1, a unary model for Fμ,ref is sketched. The main point of Section 1 is to motivate and explain the challenges that appear when we consider a language with references (Subsection 1.4 on c.o.f.e.’s is precise, the remaining subsections are somewhat sketched). Section 2 presents a relational model. This presentation is again formally precise.
Characterization of the Kidney Transcriptome of the Long-Haired Mouse Abrothrix hirta (Rodentia, Sigmodontinae) and Comparison with That of the Olive Mouse A. olivacea

To understand how small mammals cope with the challenge of water homeostasis is a matter of interest for ecologists and evolutionary biologists. Here we take a step towards the understanding of the transcriptomic functional response of kidney using as a model the long–haired mouse (Abrothrix hirta) a species that distributes across Patagonian steppes and Austral temperate rainforests in Argentina and Chile. Specifically, we i) characterize the renal transcriptome of A. hirta, and ii) compare it with that—already available—of the co-generic and co-distributed A. olivacea. Renal mRNA transcripts from 16 specimens of A. hirta from natural populations were analyzed. Over 500 million Illumina paired-end reads were assembled de novo under two approaches, an individual assembly for each specimen, and a single in-silico normalized joint assembly including all reads from all specimens. The total number of annotated genes was similar with both strategies: an average of 14,956 in individual assemblies and 14,410 in the joint assembly. Overall, 15,463 distinct genes express in the kidney of A. hirta. Transcriptomes of A. hirta and A. olivacea were similar in terms of gene abundance and composition: 95.6% of the genes of A. hirta were also found in A. olivacea making their functional profiles also similar. However, differences in the transcriptome of these two species were observed in the set of highly expressed genes, in terms of private genes for each species and the functional profiles of highly expressed genes. As part of the novel transcriptome characterization, we provide distinct gene lists with their functional annotation that would constitute the basis for further research on these or any other species of the subfamily Sigmodontinae, which includes about 400 living species distributed from Tierra del Fuego to southern United States.
Blind wavelet-based watermarking scheme using fuzzy clustering theory

In this paper, we propose an image adaptive watermarking scheme in DWT domain, by utilizing fuzzy clustering theory, the watermark can be adaptively embedded without degradation of the original image. In order to improve the robustness, we embedded watermark several times, and watermark can be extracted without referring to the original image. Simulations of various attacks will be given to demonstrate the robustness of the proposed algorithm.
Probing the Intra-Component Correlations within Fisher Vector for Material Classification

Fisher vector (FV) has become a popular image representation. One notable underlying assumption of the FV framework is that local descriptors are well decorrelated within each cluster so that the covariance matrix for each Gaussian can be simplified to be diagonal. Though the FV usually relies on the Principal Component Analysis (PCA) to decorrelate local features, the PCA is applied to the entire training data and hence it only diagonalizes the \textit{universal} covariance matrix, rather than those w.r. t. the local components. As a result, the local decorrelation assumption is usually not supported in practice. To relax this assumption, this paper proposes a completed model of the Fisher vector, which is termed as the Completed Fisher vector (CFV). The CFV is a more general framework of the FV, since it encodes not only the variances but also the correlations of the whitened local descriptors. The CFV thus leads to improved discriminative power. We take the task of material categorization as an example and experimentally show that: 1) the CFV outperforms the FV under all parameter settings; 2) the CFV is robust to the changes in the number of components in the mixture; 3) even with a relatively small visual vocabulary the CFV still works well on two challenging datasets.
EAGLE: engineering software in the ubiquitous globe by leveraging uncErtainty

In the next future we will be surrounded by a virtually infinite number of software applications that provide computational software resources in the open Globe. This will radically change the way software will be produced and used. Users will be keen on producing their own piece of software, by also reusing existing software, to better satisfy their needs, therefore with a goal oriented, opportunistic use in mind. The produced software will need to be able to evolve, react and adapt to a continuously changing environment, while guaranteeing dependability. The strongest adversary to this view is the lack of knowledge on the software's structure, behavior, and execution context. Despite the possibility to extract observational models from existing software, a producer will always operate with software artifacts that exhibit a degree of uncertainty in terms of their functional and non functional characteristics. We believe that uncertainty can only be controlled by making it explicit and by using it to drive the production process itself. In this paper, we introduce a novel paradigm of software production process that explores available software and assesses its degree of uncertainty in relation to the opportunistic goal G, assists the producer in creating the appropriate integration means towards G, and validates the quality of the integrated system with respect to G and the current context.
NBTI-aware statistical circuit delay assessment

This work establishes an analytical model framework to account for the NBTI aging effect on statistical circuit delay distribution. In this paper, we explain how circuit NBTI mitigation techniques can account for this extra variability and further present the impact of statistical PMOS NBTI DC-lifetime variability on the product delay spread.
A Survey on Techniques for Securing 6LoWPAN

The integration of low power wireless personal area networks (LoWPANs) with the Internet allows the vast number of smart objects to harvest data and information through the Internet. Such devices will also be open to many security threats from Internet as well as local network itself. To provide security from both, along with Cryptography techniques, there also requires certain mechanism which provides anonymity & privacy to the communicating parties in the network in addition to providing Confidentiality & Integrity. This paper provides survey on techniques used for securing 6LoWPAN from different attacks and aims to assist the researchers and application developers to provide baseline reference to further carry out their research in this field.
A VALIDATION OF THE CROWNE-MARLOWE SOCIAL DESIRABILITY SCALE

1. Introduction Social desirability is commonly thought of as the tendency of individuals to project favorable images of themselves during social interaction. Numerous measures of the tendency to respond in a socially desirable manner have been developed since World War II (Paulhus, 1991). One of the most commonly employed scales has been the Crowne-Marlowe (CM) Social Desirability, or Need for Approval, Scale (Crowne and Marlowe, 1960). As originally developed, this measure contains 33 true-false items that describe both acceptable but improbable behaviors, as well as those deemed unacceptable but probable. Perhaps as a consequence of the attention it has received, questions have been raised about the nature of the CM and how it functions. What we refer to as the classic social desirability interpretation suggests that the tendency to report information that is colored by social desirability concerns is best conceptualized as a personality trait which can be measured via the CM scale. A contrasting perspective, which we label the true-behavior interpretation, suggests instead that the CM reliably measures actual respondent behaviors and attitudes, rather than a propensity to edit self-reports. These competing interpretations are reviewed below.