Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust precoding for network MIMO with hierarchical CSIT

In this work1 we consider a wireless network with K cooperating transmitters (TXs) serving jointly K receivers (RXs). Due to the practical limitations of the backhaul network, it is relevant to consider a setting where each TX receives its own imperfect estimate of the multi-user channel state, denoted as the distributed channel state information (CSI) setting. We focus in this work on a particular distributed CSI configuration called hierarchical CSI configuration in which the TXs can be ordered by increasing level of CSI. This scenario is particularly relevant for future networks with heterogeneous backhaul where the TXs connected with a weak backhaul link will receive only a coarse estimate while the TXs with a stronger backhaul will have a more accurate CSI. In that scenario, we formulate the optimal precoding as a team decision problem. Solving optimally this problem is extremely challenging such that we propose a heuristic approach allowing to obtain a simple, yet efficient and practical, precoding algorithm. The proposed precoding algorithm exploits the hierarchical structure of the CSI to make the transmission more robust to the imperfect CSI knowledge at the TXs.
Maternal diet and its influence on the development of allergic disease

The early presentation of childhood allergies and the rise in their prevalence suggest that changes in early‐life exposures may increase the predisposition. Very early‐life exposures may act upon the developing foetal immune system and include infection, environmental tobacco smoke, other pollutants and nutrients provided via the mother. Three nutrients have come under close scrutiny: vitamin D, omega 3 polyunsaturated fatty acids (PUFAs) and folate (or the synthetic form, folic acid). Much of the data on these nutrients are observational although some randomised, placebo‐controlled trials have been conducted with omega 3 PUFAs and one with vitamin D. Some studies with omega 3 PUFA supplements in pregnancy have demonstrated immunomodulatory effects on the neonate and a reduction in risk of early sensitisation to allergens. A few studies with omega 3 polyunsaturated fatty acid supplements in pregnancy have shown a reduction in proportion of children affected by allergic symptoms (food allergy) or in symptom severity (atopic dermatitis). Observational studies investigating the association of maternal vitamin D intake or maternal or neonatal vitamin D status have been inconsistent. One randomised, controlled trial of vitamin D supplementation during pregnancy did not show any significant effect on allergic outcome in the offspring. Studies investigating the association between maternal folic acid or folate intake or maternal or neonatal folate status and offspring risk of allergic disease have been equivocal. Further evidence is required to clarify whether increased intake of these nutrients during pregnancy influences allergic disease in the offspring. In the light of current evidence, mothers should not either increase or avoid consuming these nutrients to prevent or ameliorate allergic disease in their offspring. However, these essential nutrients each have important roles in foetal development. This is reflected in current government recommendations for intake of these nutrients by pregnant women.
Models for Value-Added Modeling of Teacher Effects

The use of complex value-added models that attempt to isolate the contributions of teachers or schools to student development is increasing. Several variations on these models are being applied in the research literature, and policy makers have expressed interest in using these models for evaluating teachers and schools. In this article, we present a general multivariate, longitudinal mixed-model that incorporates the complex grouping structures inherent to longitudinal student data linked to teachers. We summarize the principal existing modeling approaches, show how these approaches are special cases of the proposed model, and discuss possible extensions to model more complex data structures. We present simulation and analytical results that clarify the interplay between estimated teacher effects and repeated outcomes on students over time. We also explore the potential impact of model misspecifications, including missing student covariates and assumptions about the accumulation of teacher effects over time, on key inferences made from the models. We conclude that mixed models that account for student correlation over time are reasonably robust to such misspecifications when all the schools in the sample serve similar student populations. However, student characteristics are likely to confound estimated teacher effects when schools serve distinctly different populations.
Patient, Physician, and Payment Predictors of Statin Adherence

Background:Although many patient, physician, and payment predictors of adherence have been described, knowledge of their relative strength and overall ability to explain adherence is limited. Objectives:To measure the contributions of patient, physician, and payment predictors in explaining adherence to statins. Research Design:Retrospective cohort study using administrative data. Subjects:A total of 14,257 patients insured by Horizon Blue Cross Blue Shield of New Jersey who were newly prescribed a statin cholesterol-lowering medication. Measures:Adherence to statin medication was measured during the year after the initial prescription, based on proportion of days covered. The impact of patient, physician, and payment predictors of adherence were evaluated using multivariate logistic regression. The explanatory power of these models was evaluated with C statistics, a measure of the goodness of fit. Results:Overall, 36.4% of patients were fully adherent. Older patient age, male gender, lower neighborhood percent black composition, higher median income, and fewer number of emergency department visits were significant patient predictors of adherence. Having a statin prescribed by a cardiologist, a patient's primary care physician, or a US medical graduate were significant physician predictors of adherence. Lower copayments also predicted adherence. All of our models had low explanatory power. Multivariate models including patient covariates only had greater explanatory power (C = 0.613) than models with physician variables only (C = 0.566) or copayments only (C = 0.543). A fully specified model had only slightly more explanatory power (C = 0.633) than the model with patient characteristics alone. Conclusions:Despite relatively comprehensive claims data on patients, physicians, and out-of-pocket costs, our overall ability to explain adherence remains poor. Administrative data likely do not capture many complex mechanisms underlying adherence.
An Investigation of Deep Neural Networks for Multilingual Speech Recognition Training and Adaptation

Different training and adaptation techniques for multilingual Automatic Speech Recognition (ASR) are explored in the context of hybrid systems, exploiting Deep Neural Networks (DNN) and Hidden Markov Models (HMM). In multilingual DNN training, the hidden layers (possibly extracting bottleneck features) are usually shared across languages, and the output layer can either model multiple sets of language-specific senones or one single universal IPA-based multilingual senone set. Both architectures are investigated, exploiting and comparing different language adaptive training (LAT) techniques originating from successful DNN-based speaker-adaptation. More specifically, speaker adaptive training methods such as Cluster Adaptive Training (CAT) and Learning Hidden Unit Contribution (LHUC) are considered. In addition, a language adaptive output architecture for IPA-based universal DNN is also studied and tested. Experiments show that LAT improves the performance and adaptation on the top layer further improves the accuracy. By combining state-level minimum Bayes risk (sMBR) sequence training with LAT, we show that a language adaptively trained IPA-based universal DNN outperforms a monolingually sequence trained model.
Vertebral body infarction as a confirmatory sign of spinal cord ischemic stroke: report of three cases and review of the literature.

BACKGROUND Acute spontaneous spinal cord syndromes often remain etiologically ambiguous despite extensive diagnostic efforts. In the previous literature five cases are described with acute spinal cord syndromes interpreted as spinal cord ischemic strokes because of association with vertebral body infarctions on MRI. CASE DESCRIPTIONS Three cases are presented, and the literature is reviewed. In addition to an extensive diagnostic battery including an initial MRI without pathological signs, follow-up MRI at different time intervals from the onset of symptoms showed T2 hyperintense signals in vertebral bodies. Patient 1, who had plaques in the abdominal aorta, had suffered a thoracolumbar spinal infarction; this and a concomitant infarction of the left portion of T-12 could be demonstrated on follow-up MRI on day 12. Patient 2, who had incomplete transverse spinal artery syndrome below T-3, had an abnormal signal at the T-2 level of the spinal cord on follow-up MRI on day 5; this was one segment above infarction of the dorsal area of T-3, corresponding to the ascending course of the medullary artery. The spinal cord of patient 3, who had a posterior spinal artery syndrome below T-11, was unremarkable on follow-up MRI on day 14, but a T2 hyperintense signal was noted in the dorsal area of T-10. CONCLUSIONS Vertebral body infarction represents the only confirmatory sign for the otherwise exclusionary diagnostic procedure for spinal cord ischemic stroke and must be searched for on follow-up MRI as a key to correct diagnosis.
The WD 40 protein Caf 4 p is a component of the mitochondrial fission machinery and recruits Dnm 1 p to mitochondria

he mitochondrial division machinery regulates mitochondrial dynamics and consists of Fis1p, Mdv1p, and Dnm1p. Mitochondrial division relies on the recruitment of the dynamin-related protein Dnm1p to mitochondria. Dnm1p recruitment depends on the mitochondrial outer membrane protein Fis1p. Mdv1p interacts with Fis1p and Dnm1p, but is thought to act at a late step during fission because Mdv1p is dispensable for Dnm1p localization. We identify the WD40 repeat protein Caf4p as a Fis1p-associated protein that localizes to mitochondria in a Fis1p-dependent manner. Caf4p interacts with each T component of the fission apparatus: with Fis1p and Mdv1p through its NH 2 -terminal half and with Dnm1p through its COOH-terminal WD40 domain. We demonstrate that mdv1 yeast contain residual mitochondrial fission due to the redundant activity of Caf4p. Moreover, recruitment of Dnm1p to mitochondria is disrupted in mdv1 caf4 yeast, demonstrating that Mdv1p and Caf4p are molecular adaptors that recruit Dnm1p to mitochondrial fission sites. Our studies support a revised model for assembly of the mitochondrial fission apparatus.
Proposed Edge-oriented Prediction-based Reversible Data Hiding Method ( EPRDD )

In this paper, a difference expansion based reversible data hiding approach which achieves the low distortion requirement under the same embedding capacity is proposed. An edge-oriented prediction method and a modified overflow/underflow prevention method are proposed to achieve the requirement. For each pixel, the prediction method uses edge information to get a better predicted value in an attempt to reduce the image distortion. Experimental results have demonstrated that the proposed reversible data hiding method yields lower distortion than several well-known expansion-based reversible data hiding methods.
Oral anticoagulation and dental extractions

The guidelines on oral anticoagulation from the Haemostasis and Thrombosis Task Force for the British Committee for Standards in Haematology (1998) recommended that, after dental extractions, prevention of bleeding can be achieved with oral tranexamic acid mouth rinse (4Æ8%) without dose modification of oral anticoagulants. However, tranexamic acid does not have a product licence in the UK (Evans et al, 2001) and a letter from the North-west Medicines Information Centre to the Deans of UK dental schools clearly states that tranexamic acid mouth rinse should not be used in primary dental care (Randall, 2001). To investigate whether warfarin can be continued during dental extractions, we performed a randomized controlled trial at Morriston Hospital in Swansea (Evans et al, 2002). The intervention group who continued on warfarin had a higher incidence of bleeding complications compared with the control group who stopped warfarin 2 days before extractions. However, this difference was not statistically significant. Furthermore, all but two patients controlled their minor postoperative bleeding by biting on gauze squares at home. In those two patients who attended hospital, the bleeding was stopped by local treatment of the extraction socket. Our study supports the recommendations of the Northwest Medicines Information Centre that warfarin does not need to be stopped before dental surgical procedures, providing the International Normalized Ratio of the patient is below 4. However, our study was performed in a hospital setting and it remains to be seen whether these findings can be translated to general dental practices.
Shear stress at the interface of coaxial composites determined by the resonance of low-amplitude vibrations

The aim of this work is to determine the viscoelastic behaviour of the interface in a coaxial composite material made of a tough shield and a ductile core. The elastic modulus and the amplitude-independent internal friction are measured using a longitudinal oscillating resonant system at 50 kHz. The contribution of the interface is modelled as a shear stress that modifies the elastic behaviour of the constituents. The value of this shear stress is determined for different interfaces (epoxy resin-brass, epoxy resin-Pyrex and paraffin-Pyrex). The model is autovalidated by the excellent agreement between the calculated and experimental values of the internal friction (damping) of the composites.
Software-Defined Data Shuffling for Big Data Jobs with Task Duplication

Big data jobs are usually executed on large-scale distributed computing platforms that automatically divide a job into multiple computation phases, each of which contains a number of independent tasks that can run in parallel. The data shuffling process between two consecutive phases becomes the bottleneck of job execution. To improve its performance, an approach of "push" shuffling is proposed to send intermediate results to next phase immediately once they are generated. It avoids local disk accesses in the traditional "pull" shuffling approach, and tasks in the next phase can start data processing without waiting tasks in the predecessive phase to finish. Task duplication is another approach to accelerate task execution by launching multiple task copies that compete for processing the same data block. When "push" shuffling meets task duplication, big data jobs can be significantly accelerated, but leading to a large amount of redundant data transmission between two phases. To address this challenge, we propose a software-define data shuffling approach by designing a controller and a janitor module to control the data shuffling process. Each task has a janitor that communicates with the controller to request admission permit of sending intermediate results to next-stage tasks. We further propose an online grouping algorithm to reduce the overhead of frequent communication with the controller. The performance of the proposed algorithm is evaluated by extensive simulations.
Combining Optimal Clustering and Hidden Markov Models for Extractive Summarization

We propose Hidden Markov models with unsupervised training for extractive summarization. Extractive summarization selects salient sentences from documents to be included in a summary. Unsupervised clustering combined with heuristics is a popular approach because no annotated data is required. However, conventional clustering methods such as K-means do not take text cohesion into consideration. Probabilistic methods are more rigorous and robust, but they usually require supervised training with annotated data. Our method incorporates unsupervised training with clustering, into a probabilistic framework. Clustering is done by modified K-means (MKM)---a method that yields more optimal clusters than the conventional K-means method. Text cohesion is modeled by the transition probabilities of an HMM, and term distribution is modeled by the emission probabilities. The final decoding process tags sentences in a text with theme class labels. Parameter training is carried out by the segmental K-means (SKM) algorithm. The output of our system can be used to extract salient sentences for summaries, or used for topic detection. Content-based evaluation shows that our method outperforms an existing extractive summarizer by 22.8% in terms of relative similarity, and outperforms a baseline summarizer that selects the top N sentences as salient sentences by 46.3%.
Oral infection by the bovine spongiform encephalopathy prion.

There is, however, variation in pathogenesis that is determined by factors including the interaction between host genome and agent strain. Some breeds of sheep affected by natural scrapie, for example, Montadales, have no detectable infectivity in peripheral tissues, and the distribution of infectivity in the brain may vary according to the breed of sheep (5). In bovine spongiform encephalopathy (BSE) infectivity has not been detected in peripheral tissues in natural disease, except for dorsal root ganglia and possibly bone marrow, although infectivity has been found in terminal ileum after experimental oral challenge with BSE brain (6). An important implication of this data is that, accepting the limits of the sensitivity of bioassay …
Windowed multivariate autoregressive model improving classification of labor vs. pregnancy contractions

Analyzing the propagation of uterine electrical activity is poised to become a powerful tool in labor detection and for the prediction of preterm labor. Several methods have been proposed to investigate the relationship between signals recorded externally from several sites on the pregnant uterus. A promising recent method is the multivariate autoregressive (MVAR) model. In this paper we proposed a windowed (time varying) version of the multivariate autoregressive model, called W-MVAR, to investigate the connectivity between signals while still respecting their non-stationary characteristics. The proposed method was tested on synthetic signals as well as applied to real signals. The comparison between the two methods on synthetic signals showed the superiority of W-MVAR to detect connectivity even if it is non-stationary. The application of W-MVAR on multichannel real uterine signals show that the proposed method is a good tool to distinguish non-labor and labor signals. These results are very promising and can very possibly have important clinical applications in labor detection and preterm labor prediction.
Dark Web portal overlapping community detection based on topic models

A hot research topic is the study and monitoring of online communities. Of course, homeland security institutions from many countries are using data mining techniques to perform this task, aiming to anticipate and avoid a possible menace to local peace. Tools such as social networks analysis and text mining have contributed to the understanding of these kinds of groups in order to develop counter-terrorism applications. A key application is the discovery of sub-communities of interests which main topic could be a possible homeland security threat. However, most algorithms detect disjoint communities, which means that every community member belongs to a single community. Thus, final conclusions can be omitting valuable information which leads to wrong results interpretations. In this paper, we propose a novel approach to combine traditional network analysis methods for overlapping community detection with topic-model based text mining techniques. Afterwards, we developed a sub-community detection algorithm that allow each member belong more than one sub-community. Experiments were performed using an English language based forum available in the Dark Web portal (islamicAwakening).
Leginon: an automated system for acquisition of images from vitreous ice specimens.

We have developed a system to automatically acquire cryo-electron micrographs. The system is designed to emulate all of the decisions and actions of a highly trained microscopist in collecting data from a vitreous ice specimen. These include identifying suitable areas of vitreous ice at low magnification, determining the presence and location of specimen on the grid, automatically adjusting imaging parameters (focus, astigmatism) under low-dose conditions, and acquiring images at high magnification to either film or a digital camera. This system is responsible for every aspect of image acquisition and can run unattended, other than requiring periodic refilling of the cryogens, for over 24 h. The system has been tested out on a variety of specimens that represent typical challenges in the field of cryo-electron microscopy. The results show that the overall performance of the system is equivalent to that of an experienced microscopist.
Endoscopic treatment for biliary stricture after adult living donor liver transplantation

Endoscopic intervention is considered to be the primary treatment for biliary stricture after adult living donor liver transplantation (LDLT) with duct‐to‐duct biliary reconstruction. The aim of this study was to investigate the risk factors of biliary stricture and the clinical outcomes and predictors of failure after endoscopic retrograde cholangiography with balloon dilation (ERC‐D). We enrolled 239 adult patients who underwent LDLT between 2000 and 2006. Sixty‐eight patients (28.4%) developed biliary stricture. Twenty‐nine patients with anastomotic biliary stricture were treated with ERC‐D and stenting. We retrospectively analyzed the risk factors of biliary stricture and the clinical outcomes of ERC‐D. The median follow‐up period was 31 months. The risk factors of biliary stricture on multiple logistic regression analysis were a graft with multiple bile ducts, a previous history of bile leakage, and hepatic artery stenosis. The overall success rate of ERC‐D was 64.5%. On simple logistic regression, the failure of primary ERC‐D was associated with late biliary stricture over 24 weeks and more than 8 weeks between a 2‐fold increase of serum alkaline phosphatase from the stable level and ERC‐D, even though these were not statistically significant on multiple logistic regression. The relapse rate of stricture after successful ERC‐D was 30%. The duration of stenting in the recurrence group was shorter than that in the nonrecurrence group (11.8 ± 5.03 versus 29.0 ± 11.6 weeks, P = 0.004). ERC‐D is effective for the management of anastomotic biliary stricture. However, the failure rate of primary ERC‐D may be high in patients with late onset and delayed diagnosis of biliary stricture. The recurrence seems to occur frequently in patients with a short duration of stenting. Liver Transpl 15:369–380, 2009. © 2009 AASLD.