Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-lingual Extraction and Integration of Entities, Relations, Events and Sentiments into ColdStart++ KBs with the SAFT System

This paper describes our participation in the TAC-KBP 2017 Cold Start++ Knowledge Base Population task. Our SAFT system is a loosely-coupled integration of individual components processing documents in English, Spanish and Chinese. The system extracts entities, slot relations, event nuggets and arguments, performs entity linking against Freebase and event coreference, and also integrates sentiment relations extracted by external collaborators at Columbia and Cornell. The various extractions get combined, linked, deconflicted and integrated into a consistent knowledge base (one per language) for query-based evaluation.
Tag-Driven Online Novel Recommendation with Collaborative Item Modeling

Online novel recommendation recommends attractive novels according to the preferences and characteristics of users or novels and is increasingly touted as an indispensable service of many online stores and websites. The interests of the majority of users remain stable over a certain period. However, there are broad categories in the initial recommendation list achieved by collaborative filtering (CF). That is to say, it is very possible that there are many inappropriately recommended novels. Meanwhile, most algorithms assume that users can provide an explicit preference. However, this assumption does not always hold, especially in online novel reading. To solve these issues, a tag-driven algorithm with collaborative item modeling (TDCIM) is proposed for online novel recommendation. Online novel reading is different from traditional book marketing and lacks preference rating. In addition, collaborative filtering frequently suffers from the Matthew effect, leading to ignored personalized recommendations and serious long tail problems. Therefore, item-based CF is improved by latent preference rating with a punishment mechanism based on novel popularity. Consequently, a tag-driven algorithm is constructed by means of collaborative item modeling and tag extension. Experimental results show that online novel recommendation is improved greatly by a tag-driven algorithm with collaborative item modeling.
Single Phase Current Source Inverter With Multiloop Control for Transformerless Grid–PV Interface

In this paper, a modified single-phase current source inverter (CSI) with improved multiloop control is proposed for a transformerless grid–photovoltaic interface application. A split capacitor arrangement with the middle point connected to the grid neutral and a common mode choke are used to modify the conventional CSI structure to attenuate the ground leakage current. The proposed control method permits the use of reduced value of dc side inductor by maintaining the quality of the grid current as if a high value of input inductor is used. Furthermore, virtual resistance damping is incorporated in the control loop to reduce the effect of LC resonance without compromising the efficiency, thus proving the feasibility of using CSI for the grid interface. Rigorous study, analysis, and validation of the modified CSI structure with the proposed control technique are carried out with exhaustive simulations and experiments. The experimental results on a 250 W laboratory prototype using the controller TMS320F28335 prove the effectiveness of the proposed CSI structure and its control.
Hiding Intermittent Information Leakage with Architectural Support for Blinking

As demonstrated by numerous practical attacks, the physical act of computation emits unintended and damaging information through infinitesimal variations in timing, power, and resource contention. While there are many techniques for preventing the leakage of information through power channels for specific cryptographic units, they are typically either built directly into the hardware logic or exploit intricate mathematical properties of the algorithm itself. However, such leaks are not uniform in time but, as we show, rather occur in specific bursts. Exploiting this observation we propose a set of software-controlled techniques allowing for the seamless disconnection and reconnection of general purpose programmable components in a system-on-chip. Such a system is capable of providing brief moments of electrical isolation during which the most critical computations can be performed free from both timing and power measurement. Of course, disconnection comes at a cost. To balance the resulting trade-off between overhead and security effectively, we describe a new analysis technique to uncover the "leakiest" intervals of time, we provide an algorithm to co-optimize the covering of these intervals and the performance/energy costs under a set of architecture imposed constraints, and explore the architectural and software ramifications of such intermittent disconnection. In the end we find that by hiding only between 15% and 30% of the trace, at a performance cost of between 15% and 50%, we are able to reduce the mutual information between the leakage model and key bits by 75% on average, and to nearly zero in specific cases.
Learning sentence embeddings using Recursive Networks

Learning sentence vectors that generalise well is a challenging task. In this paper we compare three methods of learning phrase embeddings: 1) Using LSTMs, 2) using recursive nets, 3) A variant of the method 2 using the POS information of the phrase. We train our models on dictionary definitions of words to obtain a reverse dictionary application similar to Felix et al. [1]. To see if our embeddings can be transferred to a new task we also train and test on the rotten tomatoes dataset [2]. We train keeping the sentence embeddings fixed as well as with fine tuning.
Étiquetage thématique automatisé de corpus par représentation sémantique

Dans les corpus de textes scientifiques, certains articles issus de communautes de chercheurs differentes peuvent ne pas etre decrits par les memes mots-cles alors qu'ils partagent la meme thematique. Ce phenomene cause des problemes dans la recherche d'information, ces articles etant mal indexes, et limite les echanges potentiellement fructueux entre disciplines scientifiques. Notre modele permet d'attribuer automatiquement une etiquette thematique aux articles au moyen d'un apprentissage des representations semantiques d'articles du corpus deja etiquetes. Passant bien a l'echelle, cette methode a pu etre testee sur une bibliotheque numerique d'articles scientifiques comportant des millions de documents. Nous utilisons un reseau semantique de synonymes pour extraire davantage d'articles semantiquement similaires et nous les fusionnons avec ceux obtenus par un modele de classement thematique. Cette methode combinee presente de meilleurs taux de rappel que les versions utilisant soit le reseau semantique seul, soit la seule representation semantique des textes.
Heterogeneity of Microbiota Dysbiosis in Chronic Rhinosinusitis: Potential Clinical Implications and Microbial Community Mechanisms Contributing to Sinonasal Inflammation

Recent studies leveraging next-generation sequencing and functional approaches to understand the human microbiota have demonstrated the presence of diverse, niche-specific microbial communities at nearly every mucosal surface. These microbes contribute to the development and function of physiologic and immunological features that are key to host health status. Not surprisingly, several chronic inflammatory diseases have been attributed to dysbiosis of microbiota composition or function, including chronic rhinosinusitis (CRS). CRS is a heterogeneous disease characterized by inflammation of the sinonasal cavity and mucosal microbiota dysbiosis. Inflammatory phenotypes and bacterial community compositions vary considerably across individuals with CRS, complicating current studies that seek to address causality of a dysbiotic microbiome as a driver or initiator of persistent sinonasal inflammation. Murine models have provided some experimental evidence that alterations in local microbial communities and microbially-produced metabolites influence health status. In this perspective, we will discuss the clinical implications of distinct microbial compositions and community-level functions in CRS and how mucosal microbiota relate to the diverse inflammatory endotypes that are frequently observed. We will also describe specific microbial interactions that can deterministically shape the pattern of co-colonizers and the resulting metabolic products that drive or exacerbate host inflammation. These findings are discussed in the context of CRS-associated inflammation and in other chronic inflammatory diseases that share features observed in CRS. An improved understanding of CRS patient stratification offers the opportunity to personalize therapeutic regimens and to design novel treatments aimed at manipulation of the disease-associated microbiota to restore sinus health.
Using cell-DEVS for prototyping unmanned aircraft system traffic simulation

The use of Unmanned Aerial Systems (UASs) is expanding speedily. This results in a need to integrate UAS traffic into non-segregated airspace. However, this integration introduces risks of mid-air collisions between UASs and manned aircraft (MA) in the airspace. To deal with these issues, we present two models that work together to assess the risk of a mid-air collision between a UAS and another manned aircraft operating in Canada's Northern airspace. The first model represents a part of Canada's Northern airspace and the aircraft and UAS traffic in it. The second model is an Uncorrelated Encounter Model (UEM) which determines whether or not a mid-air collision has occurred between a UAS and an aircraft. These two models are integrated to form a UAS-UEM model which determines the number of mid-air collisions in several UAS and aircraft flight simulations. Our results show a low probability for a mid-air collision between a UAS and an aircraft in the airspace region of interest.
Face Recognition based on Weber Symmetrical Local Graph Structure

Weber Local Descriptor (WLD) is a stable and effective feature extraction algorithm, which is based on Weber's Law. It calculates the differential excitation information and direction information, and then integrates them to get the feature information of the image. However, WLD only considers the center pixel and its contrast with its surrounding pixels when calculating the differential excitation information. As a result, the illumination variation is relatively sensitive, and the selection of the neighbor area is rather small. This may make the whole information is divided into small pieces, thus, it is difficult to be recognized. In order to overcome this problem, this paper proposes Weber Symmetrical Local Graph Structure (WSLGS), which constructs the graph structure based on the 5 × 5 neighborhood. Then the information obtained is regarded as the differential excitation information. Finally, we demonstrate the effectiveness of our proposed method on the database of ORL, JAFFE and our own built database, high-definition infrared faces. The experimental results show that WSLGS provides higher recognition rate and shorter image processing time compared with traditional algorithms.
Diabetes‐related lower‐extremity complications are a leading cause of the global burden of disability

Globally ~435 million people have diabetes [1], and an estimated 19-34% (~83-148 million people) of those will be expected to develop foot ulcers in their lifetime [2]. Foot ulcers are typically precipitated by other diabetes-related lower-extremity complications, (DRLECs) including peripheral neuropathy and peripheral vascular disease [2,3]. Collectively, DRLECs are a leading cause of infection, hospitalization and amputation outcomes [2-5], yet, these outcomes are readily preventable with evidence-based care [6,7]. This suggests the burden caused by DRLECs is a large, yet reducible, cause of the global burden of disease. This article is protected by copyright. All rights reserved.
Propositions pour améliorer une méthode de prédiction du succès d'une campagne de financement participatif

Le financement participatif est un mode de financement d'un projet faisant appel a un grand nombre de personnes qui a connu une forte croissance avec l'emergence d'Internet et des reseaux sociaux. Cependant plus de 60 % des projets ne sont pas finances, il est donc important de bien preparer sa campagne de financement. De plus, en cours de cam-pagne, il est crucial d'avoir une estimation rapide de son succes afin de pouvoir reagir rapidement (restructuration, communication) : des outils de prediction sont alors indispensables. Nous proposons dans cet article plu-sieurs pistes d'amelioration pour la prediction du montant leve lors d'une campagne de financement participatif en utilisant l'algorithme k-NN. La premiere proposition consiste a utiliser un algorithme de clustering afin de segmenter l'ensemble d'apprentissage et faciliter le passage a l'echelle. La seconde proposition consiste a extraire des caracteristiques pertinentes depuis les series temporelles et les informations sur les campagnes pour avoir une representation vectorielle.
Protected Areas and Local Communities: an Inevitable Partnership toward Successful Conservation Strategies?

Many protected areas (PAs) have followed the conventional and exclusionary approach applied at Yellowstone in 1872. As such, many parks have failed to fully integrate other important factors, such as social, cultural, and political issues. In some cases, this has triggered adverse social impacts on local communities, disrupting their traditional ways of living and limiting their control of and access to natural resources. Such an outcome can undermine protection policies through conflicts between park managers and local communities. The success of conservation strategies through protected areas may lie in the ability of managers to reconcile biodiversity conservation goals with social and economic issues and to promote greater compliance of local communities with PA conservation strategies. However, there are very few quantitative studies identifying what the key factors are that lead to better compliance with PA conservation policies. To address this issue, we conducted a meta-analysis of 55 published case studies from developing countries to determine whether the level of compliance of local communities with PA regulations was related to: (1) PA age, (2) PA area, (3) the existence of a buffer zone, (4) the level of protection as defined by IUCN categories, (5) gross domestic product per capita, (6) population density in the vicinity of PAs, and (7) the level of local community participation in PA management. We found that local community participation in the PA decision-making process was the only variable that was significantly related to the level of compliance with PA polices. In general, the higher the level of participation, the higher the level of compliance. This has important implications for PA management and suggests that greater inclusion of local communities in management should be a key strategy for ensuring the integrity of PAs.
Voltage-Gated Calcium Influx Modifies Cholinergic Inhibition of Inner Hair Cells in the Immature Rat Cochlea

Until postnatal day (P) 12, inner hair cells of the rat cochlea are invested with both afferent and efferent synaptic connections. With the onset of hearing at P12, the efferent synapses disappear, and afferent (ribbon) synapses operate with greater efficiency. This change coincides with increased expression of voltage-gated potassium channels, the loss of calcium-dependent electrogenesis, and the onset of graded receptor potentials driven by sound. The transient efferent synapses include near-membrane postsynaptic cisterns thought to regulate calcium influx through the hair cell's α9-containing and α10-containing nicotinic acetylcholine receptors. This influx activates small-conductance Ca2+-activated K+ (SK) channels. Serial-section electron microscopy of inner hair cells from two 9-d-old (male) rat pups revealed many postsynaptic efferent cisterns and presynaptic afferent ribbons whose average minimal separation in five cells ranged from 1.1 to 1.7 μm. Efferent synaptic function was studied in rat pups (age, 7–9 d) of either sex. The duration of these SK channel-mediated IPSCs was increased by enhanced calcium influx through L-type voltage-gated channels, combined with ryanodine-sensitive release from internal stores—presumably the near-membrane postsynaptic cistern. These data support the possibility that inner hair cell calcium electrogenesis modulates the efficacy of efferent inhibition during the maturation of inner hair cell synapses. SIGNIFICANCE STATEMENT Strict calcium buffering is essential for cellular function. This problem is especially acute for compact hair cells where increasing cytoplasmic calcium promotes the opposing functions of closely adjoining afferent and efferent synapses. The near-membrane postsynaptic cistern at efferent synapses segregates synaptic calcium signals by acting as a dynamic calcium store. The hair cell serves as an informative model for synapses with postsynaptic cisterns (C synapses) found in central neurons.
An Enhanced Control Scheme for an IPM Synchronous Generator Based Wind Turbine With MTPA Trajectory and Maximum Power Extraction

This paper proposes an enhanced control scheme for a direct drive variable speed wind turbine with an interior permanent magnet (IPM) synchronous generator. The proposed control scheme incorporates maximum torque per ampere (MTPA) trajectory and maximum power extraction, which ensures the generation of required torque for maximum power extraction with minimum stator current. This in turn minimizes stator copper loss and excessive heat generated in the IPM synchronous generator. The performance of the proposed control scheme has been validated through rigours simulation and experimental studies under varying wind speed conditions. The simulation and experimental results demonstrate the efficacy of the proposed control method.
Propagating labels of the human brain based on non-rigid MR image registration: an evaluation

Background: In order to perform statistical analysis of cohorts based on images, reliable methods for automated anatomical segmentation are required. Label propagation (LP) from manually segmented atlases onto newly acquired images is a particularly promising approach. Methods: We investigated LP on a set of 6 three-dimensional T1-weighted magnetic resonance data sets of the brains of normal individuals. For each image, a manually prepared segmentation of 67 structures was available. Each subject image was used in turn as an atlas and registered non-rigidly to each other subject's image. The resulting transformations were applied to the label sets, yielding five different generated segmentations for each subject, which we compared with the native manual segmentations using an overlap measure (similarity index, SI). We then reviewed the LP results for five structures with varied anatomical and label characteristics visually to determine how the registration procedure had affected the delineation of their boundaries. Results: The majority of structures propagated well as measured by SI (SI > 70 in 80% of measurements). Boundaries that were marked in the atlas image by definite intensity differences were congruent, with good agreement between the manual and the generated segmentations. Some boundaries in the manual segmentation were defined as planes marked by landmarks; such boundaries showed greater mismatch. In some cases, the proximity of structures with similar intensity distorted the LP results: e.g., parts of the parahippocampal gyrus were labeled as hippocampus in two cases. Conclusion: The size and shape of anatomical structures can be determined reliably using label propagation, especially where boundaries are defined by distinct differences in grey scale image intensity. These results will inform further work to evaluate potential clinical uses of information extracted from images in this way.
Strained Bedfellows: An Actor-Partner Analysis of Spousal Attachment Insecurity and Sleep Quality.

Background The quality of interpersonal ties-especially closer relationships-appears to be associated with physical health outcomes. Sleep is one pathway through which relationships and health appear to be linked, but this has been inadequately investigated in the context of dyadic attachment. Purpose The present study examined links between relationship-specific attachment anxiety (which can involve preoccupation with one's partner, negative relationship cognitions, and fear of abandonment) and avoidance (e.g., low emotional investment or intimacy) and sleep quality. Methods Attachment, assessed using the Experience in Close Relationship (ECR), was used to predict Pittsburgh Sleep Quality Inventory (PSQI)-assessed sleep quality in 92 married heterosexual couples via actor-partner interdependence models. Depression was examined as a potential mediator of this association. Results Consistent with hypotheses, actors' anxious attachment predicted diminished quality of their own sleep, whereas actors' avoidant attachment was unrelated to their own sleep quality. Results further suggested that couples in which both spouses were higher in attachment anxiety experience better sleep quality (b = -0.74, SE = 0.28, p = .0082, 95% CI [-1.287, -0.196]). Conversely, couples in which both spouses were higher in attachment avoidance showed poorer sleep quality (b = 0.56, SE = 0.23, p = .0188, 95% CI [0.095, 1.016]). These effects were found to be independent of marital satisfaction and depression. Some evidence was also consistent with mediation of links between attachment and sleep quality via depression. Conclusions Results suggest adult romantic attachment and sleep are associated in complex ways, highlighting the importance of dyadic approaches to the study of relationships, sleep, and health.
Personalized medicine for breast cancer: moving forward and going back.

Jeffrey S Ross1†, W Fraser Symmans2, Lajos Pusztai3 & Gabriel N Hortobagyi3 †Author for correspondence 1Albany Medical College, Department of Pathology and Laboratory Medicine, 47 New Scotland Avenue, Albany, NY 12208, USA Tel. : +1 518 262 5461; Fax: +1 518 262 3663; E-mail: rossj@mail.amc. edu 2The University of Texas M.D. Anderson Cancer Center, Division of Pathology, Houston, TX, USA 3The University of Texas M.D. Anderson Cancer Center, Division of Breast Medical Oncology, Houston, TX, USA “Rapid expansion of future novel diagnostics designed to personalize breast cancer care can easily blind us to the unmet needs for improving the accuracy and reliability of tests that are already in common daily clinical practice. ”
Semantic Annotation of Documents Based on Wikipedia Concepts

Semantic annotation is the task of augmenting an unstructured textual document with semantic information, such as concepts from an ontology. In wikification, the Wikipedia is used as an ontology and its pages (articles) are regarded as (representations of) concepts. We describe an efficient approach for annotating a document with relevant concepts from the Wikipedia. A global disambiguation method based on constructing a mention-concept graph and computing pagerank over it is used to identify a coherent set of relevant concepts considering the input document as a whole. The presented approach is suitable for parallel processing and can support any language for which a sufficiently large Wikipedia is available. Several heuristics involved in the disambiguation of candidate annotations are discussed and an experimental evaluation of their influence is presented.