Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Host and Viral Factors on Hepatitis B E Antigen-Positive Chronic Hepatitis B Patients Receiving Pegylated Interferon-α-2A Therapy

Background Pegylated interferon (PEG-IFN)-α-2a improves the hepatitis B e antigen (HBeAg) seroconversion rate in HBeAg-positive chronic hepatitis B patients. However, baseline factors predicting favourable responses to PEG-IFN-α-2a remain largely unknown. Methods A total of 115 HBeAg-positive chronic hepatitis B patients who had a pre-therapy serum alanine amino-transferase (ALT) level over two times the upper limit of normal and received PEG-IFN-α-2a for 6–12 months were consecutively enrolled according to the local reimbursed guidelines. HBeAg seroconversion and combined response defined as HBeAg seroconversion, HBV-DNA level <20,000 IU/ml as well as ALT normalization at 6 months off therapy were primary and secondary therapeutic end points, respectively. Baseline viral factors, including viral load, genotype and major sequences of precore stop codon/ basal core promoter (BCP), and host factors, including three single nucleotide polymorphisms among the HLA-DPA1, HLA-DPB1 and IL28B regions, were determined to correlate with therapeutic end points. Results HBeAg seroconversion and combined response rates were 26.1% and 18.3%, respectively. By multivariate analysis, BCP mutation (OR 8.04, 95% CI 2.00-32.28) and rs3077 G/G genotype (OR 3.49, 95% CI 1.12-10.84) were associated with a higher HBeAg seroconversion rate; BCP mutation (OR 9.28, 95% CI 1.92-44.99) and baseline viral load <2x106 IU/ml (OR 4.78, 95% CI 1.37-16.69) were associated with a higher combined response rate. Conclusions BCP mutation is associated with higher HBeAg seroconversion and combined response rates at 6 months off therapy in HBeAg-positive chronic hepatitis B patients treated with PEG-IFN-α-2a. Genetic variants in the HLA-DPA1 region may also affect treatment-induced HBeAg seroconversion.
Producing Corpora of Medieval and Premodern Occitan

At a time when the quantity of - more or less freely - available data is increasing significantly, thanks to digital corpora, editions or libraries, the development of data mining tools or deep learning methods allows researchers to build a corpus of study tailored for their research, to enrich their data and to exploit them.Open optical character recognition (OCR) tools can be adapted to old prints, incunabula or even manuscripts, with usable results, allowing the rapid creation of textual corpora. The alternation of training and correction phases makes it possible to improve the quality of the results by rapidly accumulating raw text data. These can then be structured, for example in XML/TEI, and enriched.The enrichment of the texts with graphic or linguistic annotations can also be automated. These processes, known to linguists and functional for modern languages, present difficulties for languages such as Medieval Occitan, due in part to the absence of big enough lemmatized corpora. Suggestions for the creation of tools adapted to the considerable spelling variation of ancient languages will be presented, as well as experiments for the lemmatization of Medieval and Premodern Occitan.These techniques open the way for many exploitations. The much desired increase in the amount of available quality texts and data makes it possible to improve digital philology methods, if everyone takes the trouble to make their data freely available online and reusable.By exposing different technical solutions and some micro-analyses as examples, this paper aims to show part of what digital philology can offer to researchers in the Occitan domain, while recalling the ethical issues on which such practices are based.
Homoclinic orbits, and self-excited and hidden attractors in a Lorenz-like system describing convective ﬂuid motion Homoclinic orbits, and self-excited and hidden attractors

In this paper, we discuss self-excited and hidden attractors for systems of diﬀerential equations. We considered the example of a Lorenz-like system derived from the well-known Glukhovsky– Dolghansky and Rabinovich systems, to demonstrate the analysis of self-excited and hidden attractors and their characteristics. We applied the ﬁshing principle to demonstrate the existence of a homoclinic orbit, proved the dissipativity and completeness of the system, and found absorbing and positively invariant sets. We have shown that this system has a self-excited attractor and a hidden attractor for certain parameters. The upper estimates of the Lyapunov dimension of self-excited and hidden attractors were obtained analytically.
Warm Up Cold-start Advertisements: Improving CTR Predictions via Learning to Learn ID Embeddings

Click-through rate (CTR) prediction has been one of the most central problems in computational advertising. Lately, embedding techniques that produce low-dimensional representations of ad IDs drastically improve CTR prediction accuracies. However, such learning techniques are data demanding and work poorly on new ads with little logging data, which is known as the cold-start problem. In this paper, we aim to improve CTR predictions during both the cold-start phase and the warm-up phase when a new ad is added to the candidate pool. We propose Meta-Embedding, a meta-learning-based approach that learns to generate desirable initial embeddings for new ad IDs. The proposed method trains an embedding generator for new ad IDs by making use of previously learned ads through gradient-based meta-learning. In other words, our method learns how to learn better embeddings. When a new ad comes, the trained generator initializes the embedding of its ID by feeding its contents and attributes. Next, the generated embedding can speed up the model fitting during the warm-up phase when a few labeled examples are available, compared to the existing initialization methods. Experimental results on three real-world datasets showed that Meta-Embedding can significantly improve both the cold-start and warm-up performances for six existing CTR prediction models, ranging from lightweight models such as Factorization Machines to complicated deep models such as PNN and DeepFM. All of the above apply to conversion rate (CVR) predictions as well.
New Graduate Nurse Transition Programs and Clinical Leadership Skills in Novice RNs

OBJECTIVE: The objective of this study was to determine predictors of clinical leadership skill (CLS) for RNs with 24 months of clinical experience or less. BACKGROUND: New graduate nurse transition programs (NGNTPs) have been proposed as a strategy to increase CLS. CLS is associated with positive patient outcomes. METHOD: Method used was hierarchical regression modeling to evaluate predictors of CLS among individual characteristics of RNs and characteristics of NGNTPs. RESULTS: Perceived overall quality of an NGNTP was the strongest predictor of CLS (R2 = 0.041, P < .01). Clinical experience and NGNTP characteristics accounted for 6.9% of the variance in CLS and 12.6% of the variance among RNs with assigned mentors (P < .01). RNs participating in NGNTPs for more than 24 weeks were 21 times more likely to remain employed within the organization when compared with NGNTPs of 12 weeks or less, a significant cost-benefit to the organization. CONCLUSION: Although perceived overall quality of a NGNTP was the strongest predictor of CLS, much of the variance in CLS remains unexplained.
Language Generation From Conceptual Structure: Synthesis of German in a Japanese/German MT Project

This paper describes the current state of the SEMSYN project, whose goal is to develop a module for generation of German from a semantic representation. The first application of this module is within the framework of a Japanese/German machine translation project. The generation process is organized into three stages that use distinct knowledge sources. The first stage is conceptually oriented and language independent, and exploits case and concept schemata. The second stage employs realization schemata which specify choices to map from meaning structures into German linguistic constructs. The last stage constructs the surface string using knowledge about syntax, morphology, and style. This paper describes the first two stages.
The Role of Espoused National Cultural Values in Technology Acceptance

Prior research has examined age, gender, experience, and voluntariness as the main moderators of beliefs on technology acceptance. This paper extends this line of research beyond these demographic and situational variables. Motivated by research that suggests that behavioral models do not universally hold across cultures, the paper identifies espoused national cultural values as an important set of individual difference moderators in technology acceptance. Building on research in psychological anthropology and cultural psychology that assesses cultural traits by personality tests at the individual level of analysis, we argue that individuals espouse national cultural values to differing degrees. These espoused national cultural values of masculinity/femininity, individualism/collectivism, power distance, and uncertainty avoidance are incorporated into an extended model of technology acceptance as moderators. We conducted two studies to test our model. Results indicated that, as hypothesized, social norms are stronger determinants of intended behavior for individuals who espouse feminine and high uncertainty avoidance cultural values. Contrary to expectations, espoused masculinity/femininity values did not moderate the relationship between perceived usefulness and behavioral intention but, as expected, did moderate the relationship between perceived ease of use and behavioral intention.
The Lazy Bin Packing Problem

Usually, for bin packing problems, we try to minimize the number of bins used or in the case of the dual bin packing problem, maximize the number of accepted items or accepted volume. This paper presents results for the opposite problems, where we want to maximize the number of bins used or minimize the number of accepted items or the accepted volume. For the off-line variant, we require that there must be an ordering of the bins, so that no item in a later bin fits in an earlier bin. We find the approximation ratios of two natural approximation algorithms, First-FitIncreasing and First-Fit-Decreasing. For the on-line variant, we define two different bin packing problems. In the case where the number of available bins is fixed, no on-line algorithm is competitive. For an unlimited number of bins, we find the competitive ratio of various natural algorithms.
Applying Grey Theory Prediction Model on the DGA Data of the Transformer Oil and Using It for Fault Diagnosis

This Non-Destructive Evaluation of Power transformer by monitoring various parameters, to predict its in-service behavior, is very much necessary for operating engineer to avoid catastrophic failures and costly outages. Dissolved Gas Analysis (DGA) is an important tool for transformer fault diagnosis. It is observed that the results of DGA doesn’t have a perceivable change over a short period. For less population of data availability grey modeling is used. To apply probability theory, statistics, and fuzzy systems, there is a requirement for a large number of data, then only conclusion or some inference can be drawn. The advantage of using the grey system theory is that, it gives a fair accuracy in predicting the volume of the gases, expected to be generated after some time period, using a small sample of data. In this paper we have done a comparative study on the predicted results obtained by different model of Grey theory mainly whitened model, connotation model and modified grey model. It is found that the error generated from the prediction by all the three model are within the limit of 15% which is acceptable. Additional by linear regression we are establishing a correlation between the key gases. This helps us to detect the abnormality of the situation and diagnose the type of fault. Additional this paper deals with the study on the behavior of the gas dissolved in the oil which has undergone filtration and the one without filtration. Through graphical means it has been clearly shown that filtration at periodic interval will extend the life of the transformer. It has been shown that the rate of gas generation also plays an important role to detect an active fault. Key-Words: Transformer, Dissolve gas analysis (DGA), Grey model, Modified grey model, Regression theory, Correlation, Fault diagnosis, filtration
Using Superimposed and Context Information to Find and Re-find Sub-documents

information (SI) is new information such as annotations created in reference to bits of existing information. SI references existing information using an abstraction called mark. A mark can be activated to see referenced information in its original context. Context information is the set of information obtained using a mark. For example, page number and font characteristics are included in the context information for a selection in a PDF document. Superimposed and context information can make finding and re- finding of sub-documents easier than is possible with the state of the art. For example, search results can embed marks which users can use activate to navigate directly to sub-documents; search results can include context information to potentially reduce the number of click-through operations needed to locate relevant information.
Rabbit Model of Human Gliomas: Implications for Intra-Arterial Drug Delivery

The prognosis for malignant brain tumors remains poor despite a combination of surgery, radiotherapy, and chemotherapy. This is partly due to the blood-brain barrier, a major obstacle that prevents therapeutic agents from effectively reaching the tumor. We have recently developed a method for precise and predictable opening of the blood-brain barrier via the intra-arterial administration of mannitol, a hyperosmolar agent, in a rabbit model, whose vascular anatomy facilitates the use of standard interventional neuroradiology techniques and devices. To date, however, no protocols are available that enable human glioma modeling in rabbits. In this article, we report on the xenotransplantation of a human glioblastoma (GBM-1) in adult New Zealand rabbits. We induced multi-drug immunosuppression (Mycophenolate Mofetil, Dexamethasone, Tacrolimus) and stereotactically implanted GBM-1 tumor cells into rabbit brains. The rabbits were followed for 42 days, monitored by MRI and body weight measurements, and underwent postmortem histopathological analysis. On MRI, brain tumors were identified on T2-weighted scans. On histopathology, tumors were detected with hematoxylin/eosin and their human origin was confirmed with immunohistochemistry against human-specific antigens. Our method for human glioma modeling in rabbits provides the foundation to test novel treatment strategies, including intra-arterial therapeutic agent delivery.
Active and Passive Smoking and the Risk of Subarachnoid Hemorrhage: An International Population-Based Case-Control Study

Background and Purpose— This study was undertaken to better clarify the risks associated with cigarette smoking and subarachnoid hemorrhage (SAH). Methods— The study included 432 incident cases of SAH frequency matched to 473 community SAH-free controls to determine dose-dependent associations of active and passive smoking (at home) and smoking cessation with SAH. Results— Compared with never smokers not exposed to passive smoking, the adjusted odds ratio for SAH among current smokers was 5.0 (95% confidence interval [CI], 3.1 to 8.1); for past smokers, 1.2 (95% CI, 0.8 to 2.0); and for passive smokers, 0.9 (95% CI, 0.6 to 1.5). Current and lifetime exposures showed a clear dose-dependent effect, and risks appeared more prominent in women and for aneurysmal SAH. Approximately 1 in 3 cases of SAH could be attributed to current smoking, but risks decline quickly after smoking cessation, even among heavy smokers. Conclusions— A strong positive association was found between cigarette smoking and SAH, especially for aneurysmal SAH and women, which is virtually eliminated within a few years of smoking cessation. Large opportunities exist for preventing SAH through smoking avoidance and cessation programs.
Shape Ambiguities in Structure From Motion

This paper examines the fundamental ambiguities and uncertainties inherent in recovering structure from motion. By examining the eigenvectors associated with null or small eigenvalues of the Hessian matrix, we can quantify the exact nature of these ambiguities and predict how they will affect the accuracy of the reconstructed shape. Our results for orthographic cameras show that the bas-relief ambiguity is significant even with many images, unless a large amount of rotation is present. Similar results for perspective cameras suggest that three or more frames and a large amount of rotation are required for metrically accurate reconstruction.
The Glycosyltransferase Repertoire of the Spikemoss Selaginella moellendorffii and a Comparative Study of Its Cell Wall

Spike mosses are among the most basal vascular plants, and one species, Selaginella moellendorffii, was recently selected for full genome sequencing by the Joint Genome Institute (JGI). Glycosyltransferases (GTs) are involved in many aspects of a plant life, including cell wall biosynthesis, protein glycosylation, primary and secondary metabolism. Here, we present a comparative study of the S. moellendorffii genome across 92 GT families and an additional family (DUF266) likely to include GTs. The study encompasses the moss Physcomitrella patens, a non-vascular land plant, while rice and Arabidopsis represent commelinid and non-commelinid seed plants. Analysis of the subset of GT-families particularly relevant to cell wall polysaccharide biosynthesis was complemented by a detailed analysis of S. moellendorffii cell walls. The S. moellendorffii cell wall contains many of the same components as seed plant cell walls, but appears to differ somewhat in its detailed architecture. The S. moellendorffii genome encodes fewer GTs (287 GTs including DUF266s) than the reference genomes. In a few families, notably GT51 and GT78, S. moellendorffii GTs have no higher plant orthologs, but in most families S. moellendorffii GTs have clear orthologies with Arabidopsis and rice. A gene naming convention of GTs is proposed which takes orthologies and GT-family membership into account. The evolutionary significance of apparently modern and ancient traits in S. moellendorffii is discussed, as is its use as a reference organism for functional annotation of GTs.
Homogeneous reduced moment in a gapful scalar chiral kagome antiferromagnet

We present present a quantitative experimental investigation of the scalar chiral magnetic order with in $\rm{Nd_3Sb_3Mg_2O_{14}}$. Static magnetization reveals a net ferromagnetic ground state, and inelastic neutron scattering from the hyperfine coupled nuclear spin reveals a local ordered moment of 1.76(6) $\mu_B$, just 61(2)% of the saturated moment size. The experiments exclude static disorder as the source of the reduced moment. A 38(1) $\mu$eV gap in the magnetic excitation spectrum inferred from heat capacity rules out thermal fluctuations and suggests a multipolar explanation for the moment reduction. We compare $\rm{Nd_3Sb_3Mg_2O_{14}}$ to Nd pyrochlores and show that it is close to a moment fragmented state.
Transformers with convolutional context for ASR

The recent success of transformer networks for neural machine translation and other NLP tasks has led to a surge in research work trying to apply it for speech recognition. Recent efforts studied key research questions around ways of combining positional embedding with speech features, and stability of optimization for large scale learning of transformer networks. In this paper, we propose replacing the sinusoidal positional embedding for transformers with convolutionally learned input representations. These contextual representations provide subsequent transformer blocks with relative positional information needed for discovering long-range relationships between local concepts. The proposed system has favorable optimization characteristics where our reported results are produced with fixed learning rate of 1.0 and no warmup steps. The proposed model achieves a competitive 4.7% and 12.9% WER on the Librispeech ``test clean'' and ``test other'' subsets when no extra LM text is provided.
GAP Safe screening rules for sparse multi-task and multi-class models

High dimensional regression benefits from sparsity promoting regularizations. Screening rules leverage the known sparsity of the solution by ignoring some variables in the optimization, hence speeding up solvers. When the procedure is proven not to discard features wrongly the rules are said to be safe. In this paper we derive new safe rules for generalized linear models regularized with l1 and l1/ l2 norms. The rules are based on duality gap computations and spherical safe regions whose diameters converge to zero. This allows to discard safely more variables, in particular for low regularization parameters. The GAP Safe rule can cope with any iterative solver and we illustrate its performance on coordinate descent for multi-task Lasso, binary and multinomial logistic regression, demonstrating significant speed ups on all tested datasets with respect to previous safe rules.
BOUNDARY STATES IN THE NAPPI–WITTEN MODEL

We investigate D-branes in the Nappi–Witten model. Classically symmetric D-branes are classified by the (twisted) conjugacy classes of the Nappi–Witten group, which specify the geometry of the corresponding D-branes. Quantum description of the D-branes is given by boundary states, and we need one-point functions of closed strings to construct the boundary states. We compute the one-point functions solving conformal bootstrap constraints, and check that the classical limit of the boundary states reproduces the geometry of D-branes.
Adaptive Processor Allocation in Packet Processing Systems

The functionality of packet processing applications is often partitioned into pipeline stages; these stages are allocated a subset of the multiple processors available in a packet processing system. The workload, and hence the processing requirement, for each pipeline stage fluctuates over time. Adapting processor allocations to pipeline stages at run-time can improve robustness of the system to traffic fluctuations, can reduce processor provisioning requirement of the system, and can conserve energy. In this paper, we present an on-line algorithm for adapting processor allocations while ensuring that the additional delay suffered by packets as a result of adaptation is deterministically bounded. The resulting Processor Allocation Algorithm (PAL) is simple, but it allocates only as many processors to stages as needed to meet packet delay guarantees, accounts for system reconfiguration overheads, and copes with the unpredictability of packet arrival patterns. A key contribution of PAL is its generality; it captures the adaptation opportunities in the system as a finite state automaton (FSA)—the methodology for constructing the FSA can be applied to a variety of application requirements and system configurations. We demonstrate that for a set of trace workloads PAL can reduce processor provisioning level by 30-50%, reduce energy consumption by 60-70% while increasing the average packet processing delay by less than 150μs. We describe our prototype implementation for Intel’s IXP2400-based packet processing system.
Chronic kidney disease in Central America: the big picture.

A letter to the editor is presented in response to the article "The Epidemic of Chronic Kidney Disease of Unknown Etiology in Mesoamerica: A Call for Interdisciplinary Research and Action" by Catharina Wesseling, Jennifer Crowe, Christer Hogstedt, Kristina Jakobsson, Rebekah Lucas, and David Wegman, which appeared in the November 2013 issue, with a reply from the authors.