Miracle-dz/UniFAR-FTUs huggingface dataset:
Facet Annotation Using Reference Knowledge Bases

Faceted interfaces are omnipresent on the web to support data exploration and filtering. A facet is a triple: a domain (e.g., Book), a property (e.g., author, language), and a set of property values (e.g., Austen, Beauvoir, Coelho, Dostoevsky, Eco, Kerouac, Suskind, ., French, English, German, Italian, Portuguese, Russian, . ). Given a property (e.g., language), selecting one or more of its values (English and Italian) returns the domain entities (of type Book) that match the given values (the books that are written in English or Italian). To implement faceted interfaces in a way that is scalable to very large datasets, it is necessary to automate facet extraction. Prior work associates a facet domain with a set of homogeneous values, but does not annotate the facet property. In this paper, we annotate the facet property with a predicate from a reference Knowledge Base (KB) so as to maximize the semantic similarity between the property and the predicate. We define semantic similarity in terms of three new metrics: specificity, coverage, and frequency. Our experimental evaluation uses the DBpedia and YAGO KBs and shows that for the facet annotation problem, we obtain better results than a state-of-the-art approach for the annotation of web tables as modified to annotate a set of values.
Reinforced Co-Training

Co-training is a popular semi-supervised learning framework to utilize a large amount of unlabeled data in addition to a small labeled set. Co-training methods exploit predicted labels on the unlabeled data and select samples based on prediction confidence to augment the training. However, the selection of samples in existing co-training methods is based on a predetermined policy, which ignores the sampling bias between the unlabeled and the labeled subsets, and fails to explore the data space. In this paper, we propose a novel method, Reinforced Co-Training, to select high-quality unlabeled samples to better co-train on. More specifically, our approach uses Q-learning to learn a data selection policy with a small labeled dataset, and then exploits this policy to train the co-training classifiers automatically. Experimental results on clickbait detection and generic text classification tasks demonstrate that our proposed method can obtain more accurate text classification results.
LoST? Appearance-Invariant Place Recognition for Opposite Viewpoints using Visual Semantics

Human visual scene understanding is so remarkable that we are able to recognize a revisited place when entering it from the opposite direction it was first visited, even in the presence of extreme variations in appearance. This capability is especially apparent during driving: a human driver can recognize where they are when travelling in the reverse direction along a route for the first time, without having to turn back and look. The difficulty of this problem exceeds any addressed in past appearance- and viewpoint-invariant visual place recognition (VPR) research, in part because large parts of the scene are not commonly observable from opposite directions. Consequently, as shown in this paper, the precision-recall performance of current state-of-the-art viewpoint- and appearance-invariant VPR techniques is orders of magnitude below what would be usable in a closed-loop system. Current engineered solutions predominantly rely on panoramic camera or LIDAR sensing setups; an eminently suitable engineering solution but one that is clearly very different to how humans navigate, which also has implications for how naturally humans could interact and communicate with the navigation system. In this paper we develop a suite of novel semantic- and appearance-based techniques to enable for the first time high performance place recognition in this challenging scenario. We first propose a novel Local Semantic Tensor (LoST) descriptor of images using the convolutional feature maps from a state-of-the-art dense semantic segmentation network. Then, to verify the spatial semantic arrangement of the top matching candidates, we develop a novel approach for mining semantically-salient keypoint correspondences.
Signaling Games and the Evolution of Structure in Language and Music: A Reply to Ravignani and Verhoef (2018)‡

In their commentary on our work, Ravignani and Verhoef (2018) raise concerns about two methodological aspects of our experimental paradigm (the signaling game): (1) the use of melodic signals of fixed length and duration, and (2) the fact that signals are endowed with meaning. They argue that music is hardly a semantic system and that our methodological choices may limit the capacity of our paradigm to shed light on the emergence and evolution of a number of putative musical universals. We reply that musical systems are semantic systems and that the aim of our research is not to study musical universals as such, but to compare more closely the kinds of principles that organize meaning and structure in linguistic and musical systems.
SemEval-2013 Task 7: The Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge

We present the results of the Joint Student Response Analysis and 8th Recognizing Textual Entailment Challenge, aiming to bring together researchers in educational NLP technology and textual entailment. The task of giving feedback on student answers requires semantic inference and therefore is related to recognizing textual entailment. Thus, we offered to the community a 5-way student response labeling task, as well as 3-way and 2way RTE-style tasks on educational data. In addition, a partial entailment task was piloted. We present and compare results from 9 participating teams, and discuss future directions.
Observations of Reconnection Flows in a Flare on the Solar Disk

Magnetic reconnection is a well-accepted part of the theory of solar eruptive events, though the evidence is still circumstantial. Intrinsic to the reconnection picture of a solar eruptive event, particularly in the standard model for two-ribbon flares (CSHKP model), are an advective flow of magnetized plasma into the reconnection region, expansion of field above the reconnection region as a flux rope erupts, retraction of heated post-reconnection loops, and downflows of cooling plasma along those loops. We report on a unique set of Solar Dynamics Observatory/Atmospheric Imaging Assembly imaging and Hinode/EUV Imaging Spectrometer spectroscopic observations of the disk flare SOL2016-03-23T03:54 in which all four flows are present simultaneously. This includes spectroscopic evidence for a plasma upflow in association with large-scale expanding closed inflow field. The reconnection inflows are symmetric, and consistent with fast reconnection, and the post-reconnection loops show a clear cooling and deceleration as they retract. Observations of coronal reconnection flows are still rare, and most events are observed at the solar limb, obscured by complex foregrounds, making their relationship to the flare ribbons, cusp field, and arcades formed in the lower atmosphere difficult to interpret. The disk location and favorable perspective of this event have removed these ambiguities giving a clear picture of the reconnection dynamics.
Structural Brain Alterations in Patients with Lumbar Disc Herniation: A Preliminary Study

Chronic pain is one of the most common health complaints in industrial nations. For example, chronic low back pain (cLBP) disables millions of people across the world and generates a tremendous economic burden. While previous studies provided evidence of widespread functional as well as structural brain alterations in chronic pain, little is known about cortical changes in patients suffering from lumbar disc herniation. We investigated morphometric alterations of the gray and white matter of the brain in patients suffering from LDH. The volumes of the gray and white matter of 12 LDH patients were determined in a prospective study and compared to the volumes of healthy controls to distinguish local differences. High-resolution MRI brain images of all participants were performed using a 3 Tesla MRI scanner. Voxel-based morphometry was used to investigate local differences in gray and white matter volume between patients suffering from LDH and healthy controls. LDH patients showed significantly reduced gray matter volume in the right anterolateral prefrontal cortex, the right temporal lobe, the left premotor cortex, the right caudate nucleus, and the right cerebellum as compared to healthy controls. Increased gray matter volume, however, was found in the right dorsal anterior cingulate cortex, the left precuneal cortex, the left fusiform gyrus, and the right brainstem. Additionally, small subcortical decreases of the white matter were found adjacent to the left prefrontal cortex, the right premotor cortex and in the anterior limb of the left internal capsule. We conclude that the lumbar disk herniation can lead to specific local alterations of the gray and white matter in the human brain. The investigation of LDH-induced brain alterations could provide further insight into the underlying nature of the chronification processes and could possibly identify prognostic factors that may improve the conservative as well as the operative treatment of the LDH.
An international perspective on educators' perceptions of children with Traumatic Brain Injury.

BACKGROUND Educators lack understanding of traumatic brain injury (TBI), which can lead to a lack of appropriate assessment and intervention methods for these students. OBJECTIVE(S) This qualitative study explored what experienced teachers perceive, believe, and know about pediatric TBI. METHODS Following development of a standardized interview protocol, 46 teachers from Australia, New Zealand, Northern Ireland, and the United States took part in semi-structured interviews. Topics included understanding of the effects of TBI on school performance, supporting a child with TBI in the classroom, and challenges and teaching efficacy in working with students with TBI. RESULTS The themes we identified were: personal experience with TBI, lack of content knowledge, non-TBI-specific adaptations, collaboration with experts, need for specific training, confidence in working with students with TBI, and knowledge of students' rights to service provision. Our findings show that although teachers had little knowledge of TBI, many felt they would be able to adequately support a child with appropriate input from specialists. CONCLUSION Teachers fill their knowledge gaps about TBI with their own personal experiences and prior information about working with students with disabilities. These findings support important implications for changes in how we educate and support teachers.
A Method for Generation Phage Cocktail with Great Therapeutic Potential

Background Bacteriophage could be an alternative to conventional antibiotic therapy against multidrug-resistant bacteria. However, the emergence of resistant variants after phage treatment limited its therapeutic application. Methodology/Principal Findings In this study, an approach, named “Step-by-Step” (SBS), has been established. This method takes advantage of the occurrence of phage-resistant bacteria variants and ensures that phages lytic for wild-type strain and its phage-resistant variants are selected. A phage cocktail lytic for Klebsiella pneumoniae was established by the SBS method. This phage cocktail consisted of three phages (GH-K1, GH-K2 and GH-K3) which have different but overlapping host strains. Several phage-resistant variants of Klebsiella pneumoniae were isolated after different phages treatments. The virulence of these variants was much weaker [minimal lethal doses (MLD)>1.3×109 cfu/mouse] than that of wild-type K7 countpart (MLD = 2.5×103 cfu/mouse). Compared with any single phage, the phage cocktail significantly reduced the mutation frequency of Klebsiella pneumoniae and effectively rescued Klebsiella pneumoniae bacteremia in a murine K7 strain challenge model. The minimal protective dose (MPD) of the phage cocktail which was sufficient to protect bacteremic mice from lethal K7 infection was only 3.0×104 pfu, significantly smaller (p<0.01) than that of single monophage. Moreover, a delayed administration of this phage cocktail was still effective in protection against K7 challenge. Conclusions/Significance Our data showed that the phage cocktail was more effective in reducing bacterial mutation frequency and in the rescue of murine bacteremia than monophage suggesting that phage cocktail established by SBS method has great therapeutic potential for multidrug-resistant bacteria infection.
Moving Convolutional Neural Networks to Embedded Systems: The AlexNet and VGG-16 Case

Execution of deep learning solutions is mostly restricted to high performing computing platforms, e.g., those endowed with GPUs or FPGAs, due to the high demand on computation and memory such solutions require. Despite the fact that dedicated hardware is nowadays subject of research and effective solutions exist, we envision a future where deep learning solutions -here Convolutional Neural Networks (CNNs)- are mostly executed by low-cost off-the shelf embedded platforms already available in the market. This paper moves in this direction and aims at filling the gap between CNNs and embedded systems by introducing a methodology for the design and porting of CNNs to limited in resources embedded systems. In order to achieve this goal we employ approximate computing techniques to reduce the computational load and memory occupation of the deep learning architecture by compromising accuracy with memory and computation. The proposed methodology has been validated on two well-know CNNs, i.e., AlexNet and VGG-16, applied to an image-recognition application and ported to two relevant off-the-shelf embedded platforms.
Kisspeptin and Cancer: Molecular Interaction, Biological Functions, and Future Perspectives

Cancer disease is the second leading cause of death in the world and one of the main fields of medical research. Although there is now a greater understanding of biological mechanisms of uncontrolled cell growth, invasiveness and metastasization, the multi-step process of cancer development and evolution is still incompletely understood. The inhibition of molecules activated in cancer metastasization is an hot topic in cancer research. Among the known antimetastatic genes, KiSS-1 is involved in the metastatic cascade by preventing growth of metastasis. Moreover, loss of KiSS-1 protein expression by tumor cells has been associated with a more aggressive phenotype. KiSS-1 gene encodes a 145-amino acid protein, which following proteolytic cleavage, generates a family of kisspeptins (Kp-10, -13, and -14), that are endogenous agonists for the G-protein-coupled receptor (GPR54). The antitumor effect of KiSS-1 was primarily associated with the inhibition of proliferation, migration and cell invasion and, consequently, the reduced formation of metastasis and intratumoral microvessels. In this review, we highlight the latest data on the role of kisspeptin signaling in the suppression of metastasis in various cancer types and the use modulators of KiSS/GPR54 signaling as potential novel therapeutic agents for the treatment of cancer.
Large-Scale Hierarchical Text Classification with Recursively Regularized Deep Graph-CNN

Text classification to a hierarchical taxonomy of topics is a common and practical problem. Traditional approaches simply use bag-of-words and have achieved good results. However, when there are a lot of labels with different topical granularities, bag-of-words representation may not be enough. Deep learning models have been proven to be effective to automatically learn different levels of representations for image data. It is interesting to study what is the best way to represent texts. In this paper, we propose a graph-CNN based deep learning model to first convert texts to graph-of-words, and then use graph convolution operations to convolve the word graph. Graph-of-words representation of texts has the advantage of capturing non-consecutive and long-distance semantics. CNN models have the advantage of learning different level of semantics. To further leverage the hierarchy of labels, we regularize the deep architecture with the dependency among labels. Our results on both RCV1 and NYTimes datasets show that we can significantly improve large-scale hierarchical text classification over traditional hierarchical text classification and existing deep models.
Prospective risk of osteoporotic fractures in patients with advanced chronic obstructive pulmonary disease

Despite the high prevalence of osteoporosis in chronic obstructive pulmonary disease (COPD) patients, the fracture risk prediction tools are not routinely undertaken in the management of COPD. We quantified fracture risk using a validated risk prediction tool (Fracture Risk Assessment (FRAX®)) and determined potential bone-protection treatment needs in patients with advanced COPD. The 10-year probability of major osteoporotic or hip fracture was calculated using the FRAX tool in a cohort of patients attending a hospital complex COPD service. Patients were identified to be at low, intermediate and high risk based on their FRAX scores, in accordance with the National Osteoporosis Guideline Group recommendations, to assess the number of patients requiring bone mineral density (BMD) testing or bone protection therapy. Two hundred forty-seven patients [mean (standard deviation (SD)) age 66 (9.1) years, 26% current smokers, 40% women and median (interquartile range (IQR)) Medical Research Council (MRC) breathlessness scale 4 (0)] had a 10-year probability of 9.5% (6.1) and 3.8% (4.6) for major osteoporotic and hip fractures, respectively. Thirty-six percentage of patients were identified to be at intermediate risk of developing fragility fracture, requiring BMD assessment, while 9% were at high risk, requiring treatment. Thirty-two percentage of high-risk patients were on bisphosphonates. The FRAX score can be used to assess the fracture risk within the COPD cohort and assist with decision-making about BMD measurement and provision of bone protection therapy.
Human Perception of Inertial Mass for Joint Human-Robot Object Manipulation

In this article, we investigate human perception of inertial mass discrimination in active planar manipulations, as they are common in daily tasks, such as moving heavy and bulky objects. Psychophysical experiments were conducted to develop a human inertial mass perception model to improve usability and acceptance of novel haptically collaborating robotic systems. In contrast to existing literature, large-scale movements involving a broad selection of reference stimuli and larger sample sizes were used. Linear mixed models were fitted to model dependent errors from the longitudinal perceptual data. Differential thresholds near the perception boundary exponentially increased and resulted in constant behavior for higher stimuli. No effect of different directions (sagittal and transversal) was found; however, a large effect of different movement types (precise and imprecise) was present in the data. Recommendations to implement the findings in novel physical assist devices are given.
Efficacy of continuous versus intermittent subglottic secretion drainage in preventing ventilator-associated pneumonia in patients requiring mechanical ventilation: A single-center randomized controlled trial

Objective Aspiration of subglottic secretion is a widely used intervention to prevent ventilator-associated pneumonia (VAP). This study aimed to compare the efficacy of continuous and intermittent subglottic secretion drainage (SSD) in preventing VAP. Methods A single-center randomized controlled trial was conducted on adult postoperative patients who were expected to undergo mechanical ventilation for more than 48 hours. Primary outcome measure was incidence of VAP and secondary outcome measures were length of mechanical ventilation and intensive-care unit (ICU) stay. Results Fifty-nine patients received continuous SSD, while 60 patients received intermittent SSD. Of these 119 patients, 88 (74%) were excluded and 15 and 16 patients were allocated to receive continuous and intermittent SSD, respectively. VAP was detected in 4 (26.7%) and 7 (43.8%) patients in the continuous and intermittent groups, respectively, (p=0.320). The length of mechanical ventilation was significantly shorter (p=0.034) in the continuous group (99.5±47.1 h) than in the intermittent group (159.9±94.5 h). The length of ICU stay was also shorter (p=0.0097) in the continuous group (6.3±2.1 days) than the intermittent group (9.8±4.8 days). Conclusions Although continuous SSD did not reduce the incidence of VAP, it reduced the length of mechanical ventilation and ICU stay when compared to intermittent SSD.
Effects of molybdenum on expression of cold-responsive genes in abscisic acid (ABA)-dependent and ABA-independent pathways in winter wheat under low-temperature stress.

BACKGROUND AND AIMS Molybdenum (Mo) is an essential trace element for higher plants. It has been shown that application of Mo enhances the cold resistance of winter wheat. In order to improve our understanding of the molecular mechanisms of cold resistance arising from application of Mo in winter wheat, investigations were made regarding the transcription of cold-responsive (COR) genes in abscisic acid (ABA)-dependent and ABA-independent pathways in winter wheat regulated by Mo application under low-temperature stress. METHODS Two cultivars of winter wheat (Triticum aestivum), Mo-efficient cultivar '97003' and Mo-inefficient cultivar '97014', were grown in control (-Mo) and Mo fertilizer (+Mo) treatments for 40 d at 15/12 degrees C (day/night), and the temperature was then reduced to 5/2 degrees C (day/night) to create low-temperature stress. Aldehyde oxidase (AO) activities, ABA contents, the transcripts of basic leucine zipper (bZIP)-type transcription factor (TF) genes, ABA-dependent COR genes, CBF/DREB transcription factor genes and ABA-independent COR genes were investigated at 0, 3, 6 and 48 h post cold stress. KEY RESULTS Mo application significantly increased AO activity, ABA levels, and expression of bZIP-type TF genes (Wlip19 and Wabi5) and ABA-dependent COR genes (Wrab15, Wrab17, Wrab18 and Wrab19). Mo application increased expression levels of CBF/DREB transcription factor genes (TaCBF and Wcbf2-1) and ABA-independent COR genes (Wcs120, Wcs19, Wcor14 and Wcor15) after 3 and 6 h exposure to low temperature. CONCLUSIONS Mo might regulate the expression of ABA-dependent COR genes through the pathway: Mo --> AO --> ABA --> bZIP --> ABA-dependent COR genes in winter wheat. The response of the ABA-dependent pathway to Mo was prior to that of the ABA-independent pathway. Similarities and differences between the Mo-efficient and Mo-inefficient wheat cultivars in response to Mo under cold stress are discussed.
Gender Gaps in Letter-Sound Knowledge Persist Across the First School Year

Literacy is the cornerstone of a primary school education and enables the intellectual and social development of young children. Letter-sound knowledge has been identified as critical for developing proficiency in reading. This study explored the development of letter-sound knowledge in relation to gender during the first year of primary school. 485 Norwegian children aged 5–6 years completed assessment of letter-sound knowledge, i.e., uppercase letters- name; uppercase letter -sound; lowercase letters- name; lowercase letter-sound. The children were tested in the beginning, middle, and end of their first school year. The results revealed a clear gender difference in all four variables in favor of the girls which were relatively constant over time. Implications for understanding the role of gender and letter-sound knowledge for later reading performance are discussed.