Miracle-dz/UniFAR-FTUs huggingface dataset:
Cleaning noisy wordnets

Automatic approaches to creating and extending wordnets, which have become very popular in the past decade, inadvertently result in noisy synsets. This is why we propose an approach to detect synset outliers in order to eliminate the noise and improve accuracy of the developed wordnets, so that they become more useful lexico-semantic resources for natural language applications. The approach compares the words that appear in the synset and its surroundings with the contexts of the literals in question they are used in based on large monolingual corpora. By fine-tuning the outlier threshold we can influence how many outlier candidates will be eliminated. Although the proposed approach is language-independent we test it on Slovene and French that were created automatically from bilingual resources and contain plenty of disambiguation errors. Manual evaluation of the results shows that by applying a threshold similar to the estimated error rate in the respective wordnets, 67% of the proposed outlier candidates are indeed incorrect for French and a 64% for Slovene. This is a big improvement compared to the estimated overall error rates in the resources, which are 12% for French and 15% for Slovene.
MetaSeg: MetaFormer-based Global Contexts-aware Network for Efficient Semantic Segmentation

Beyond the Transformer, it is important to explore how to exploit the capacity of the MetaFormer, an architecture that is fundamental to the performance improvements of the Transformer. Previous studies have exploited it only for the backbone network. Unlike previous studies, we explore the capacity of the Metaformer architecture more extensively in the semantic segmentation task. We propose a powerful semantic segmentation network, MetaSeg, which leverages the Metaformer architecture from the backbone to the decoder. Our MetaSeg shows that the MetaFormer architecture plays a significant role in capturing the useful contexts for the decoder as well as for the backbone. In addition, recent segmentation methods have shown that using a CNN-based backbone for extracting the spatial information and a decoder for extracting the global information is more effective than using a transformer-based backbone with a CNN-based decoder. This motivates us to adopt the CNN-based backbone using the MetaFormer block and design our MetaFormer-based decoder, which consists of a novel self-attention module to capture the global contexts. To consider both the global contexts extraction and the computational efficiency of the self-attention for semantic segmentation, we propose a Channel Reduction Attention (CRA) module that reduces the channel dimension of the query and key into the one dimension. In this way, our proposed MetaSeg out-performs the previous state-of-the-art methods with more efficient computational costs on popular semantic segmentation and a medical image segmentation benchmark, including ADE20K, Cityscapes, COCO-stuff, and Synapse.
Measurement of Deuterium Oxide by Infrared Spectroscopy and Isotope Ratio Mass Spectrometry for Quantifying Daily Milk Intake in Breastfed Infants and Maternal Body Fat

Measurements of deuterium oxide concentrations (DOC) in saliva by infrared spectroscopy (IRS) and isotope ratio mass spectrometry (IRMS) were compared for the calculation of infants’ milk intake and maternal body fat. Deuterium oxide (30g) was given to the mother. DOC by IRS and IRMS were 340 ± 209 and 345 ± 202 ppm. The mean difference between both methods (4.62 ± 76.3) was not different from zero (p = .57). Bland and Altman analysis showed no significant overall bias (r = 0.10; p = .33), but dispersion within the limits of agreement. The mean milk intake in the infants’ by IRS and IRMS were 624 ± 296 g/day and 634 ± 327 g/day (p = .642) and the mothers’ mean body fat were 31.3 ± 12% and 31.5 ± 13% (p = .755). The results appear to be the same for milk intake and body fat by the two methods and no evidence of bias was found.
Concepts of functioning and health important to people with systemic sclerosis: a qualitative study in four European countries

Objective To describe the experiences of people with systemic sclerosis (SSc) in different European countries of functioning and health and to link these experiences to the WHO International Classification of Functioning, Disability and Health (ICF) to develop a common understanding from a bio-psycho-social perspective. Method A qualitative multicentre study with focus-group interviews was performed in four European countries: Austria, Romania, Sweden and Switzerland. The qualitative data analysis followed a modified form of ‘meaning condensation’ and the concepts that emerged in the analysis were linked to the ICF. Results 63 people with SSc participated in 13 focus groups. In total, 86 concepts were identified. 32 (37%) of these were linked to the ICF component body functions and structures, 21 (24%) to activities and participation, 26 (30%) to environmental factors, 6 (7%) to personal factors and 1 (1%) to the health condition itself. 19 concepts (22%) were identified in all four countries and included impaired hand function, household activities, paid work, drugs, climate and coldness, support from others and experiences with healthcare institutions, non-pharmacological treatment, social security and benefits. Conclusion Concepts identified in all four countries could be used for guiding clinical assessment, as well as interdisciplinary team care and rheumatological rehabilitation for patients with SSc. For a full understanding of the aspects of the disease that were most relevant to people with SSc, people with SSc from multiple countries needed to be involved.
Fast Multiple-Part Based Object Detection Using KD-Ferns

In this work we present a new part-based object detection algorithm with hundreds of parts performing real-time detection. Part-based models are currently state-of-the-art for object detection due to their ability to represent large appearance variations. However, due to their high computational demands such methods are limited to several parts only and are too slow for practical real-time implementation. Our algorithm is an accelerated version of the "Feature Synthesis'' (FS) method[1], which uses multiple object parts for detection and is among state-of-the-art methods on human detection benchmarks, but also suffers from a high computational cost. The proposed Accelerated Feature Synthesis (AFS) uses several strategies for reducing the number of locations searched for each part. The first strategy uses a novel algorithm for approximate nearest neighbor search which we developed, termed "KD-Ferns'', to compare each image location to only a subset of the model parts. Candidate part locations for a specific part are further reduced using spatial inhibition, and using an object-level "coarse-to-fine'' strategy. In our empirical evaluation on pedestrian detection benchmarks, AFS maintains almost fully the accuracy performance of the original FS, while running more than X4 faster than existing part-based methods which use only several parts. AFS is to our best knowledge the first part-based object detection method achieving real-time running performance: nearly 10 frames per-second on 640X480 images on a regular CPU.
Massive Infrastructure Development and Its Impact on Indonesia’s Economy

for regional multiplier effects show that North Sumatra and South Sulawesi provinces had the highest multiplier values. Sectoral analysis then shows that economic and industrial zones, bridges and roads, and electric power had the greatest economic and labour impacts. Due to data availability, the study focusses on the impact of construction activities, although impacts from operations may increase the multiplier effects.
MOSCFRA : A Genetic Approach for Simultanious Clustering and Gene Ranking

Microarray experiments generate a large amount of data which is used to discover the genetic background of diseases and to know the gene characteristics. Clustering the tissue samples is an important tool for partitioning the dataset according to co-expression patterns. This clustering task is even more difficult when we try to find the rank of each gene (Gene Ranking) according to their abilities to distinguish different classes of samples. Finding clusters for samples and rank of each gene for a specific gene expression data in a single process is always better. In the literature many algorithms are available for finding the clusters and gene ranking or selection separately. A few algorithms for simultaneous clus- tering and feature selection are also available. In this article, we propose a new approach to cluster the samples and rank the genes, simultane- ously. A novel encoding technique is proposed here for the problem of simultaneous clustering and ranking. Results have been demonstrated for both artificial and real-life gene expression data sets.
10 The “ Emotional Side ” of Subjective Tinnitus

Roberto Teggi1, Daniela Caldirola2, Giampaolo Perna2,3 and Mario Bussi1 1Department of Otolaryngology, Vita-Salute University, San Raffaele Hospital, Milan, 2Department of Clinical Neuroscience, San Benedetto Hospital, Hermanas Hospitalarias, Albese con Cassano, 3Department of Psychiatry and Neuropsychology, Faculty of Health, Medicine and Life Sciences, University of Maastricht, Maastricht, 1,2Italy 3The Netherlands
Microcomputer controlled buck regulator for maximum power point tracker for DC pumping system operates from photovoltaic system

A design of an inexpensive and efficient maximum power point tracker is presented. This design calls for a pilot module to track the maximum power point voltage (V/sub m/). The tracking is done by changing the duty cycle of a step-down chopper, which is controlled by a software P voltage controller. The controlled voltage of the tracker (V/sub m/) is obtained by measuring the open circuit voltage (V/sub oc/) and searching in the look-up table stored in the computer for (V/sub m/) versus the measured (V/sub oc/). This table is obtained from theoretical calculations for maximum power points locus of the solar cell array (SCA) used. For the difference between the theoretical and experimental locus of the maximum power points, for temperature effect, degradation of the (SCA) and dust effect, the oscillation about the theoretical point is used to determine the actual point. It is also used for the (power-voltage) curve of the SCA.
Electrospun Polymeric Scaffolds with Enhanced Biomimetic Properties for Tissue Engineering Applications

This PhD Thesis is focused on the development of fibrous polymeric scaffolds for tissue engineering applications and on the improvement of scaffold biomimetic properties. Scaffolds were fabricated by electrospinning, which allows to obtain scaffolds made of polymeric micro or nanofibers. Biomimetism was enhanced by following two approaches: (1) the use of natural biopolymers, and (2) the modification of the fibers surface chemistry. Gelatin was chosen for its bioactive properties and cellular affinity, however it lacks in mechanical properties. This problem was overcome by adding poly(lactic acid) to the scaffold through co-electrospinning and mechanical properties of the composite constructs were assessed. Gelatin effectively improves cell growth and viability and worth noting, composite scaffolds of gelatin and poly(lactic acid) were more effective than a plain gelatin scaffold. Scaffolds made of pure collagen fibers were fabricated. Modification of collagen triple helix structure in electrospun collagen fibers was studied. Mechanical properties were evaluated before and after crosslinking. The crosslinking procedure was developed and optimized by using - for the first time on electrospun collagen fibers - the crosslinking reactant 1,4-butanediol diglycidyl ether, with good results in terms of fibers stabilization. Cell culture experiments showed good results in term of cell adhesion and morphology. The fiber surface chemistry of electrospun poly(lactic acid) scaffold was modified by plasma treatment. Plasma did not affect thermal and mechanical properties of the scaffold, while it greatly increased its hydrophilicity by the introduction of carboxyl groups at the fiber surface. This fiber functionalization enhanced the fibroblast cell viability and spreading. Surface modifications by chemical reactions were conducted on electrospun scaffolds made of a polysophorolipid. The aim was to introduce a biomolecule at the fiber surface. By developing a series of chemical reactions, one oligopeptide every three repeating units of polysophorolipid was grafted at the surface of electrospun fibers.
Influence of iodine in excess on seminiferous tubular structure and epididymal sperm character in male rats

Excess iodine induced public health problems are now emerging in many iodine sufficient regions for indiscriminate intake of iodine through various iodized products. It has been reported that excess iodine can disrupt overall male reproductive physiology by generating oxidative stress in the testis. However, information on the possible effect of iodine in excess on spermatozoa found less. In the present investigation flow cytometric techniques and scanning electron microscopy (SEM) have been used to study the spermatozoal functional as well as structural status under the influence of excess iodine; generation of ROS in the spermatozoa as evident by DCFDA, altered acrosomal integrity as observed by fluorescence lectin staining method and depolarized mitochondrial membrane potential (ΔΨm) noticed by JC‐1 staining. Ultrastructure of seminiferous tubule after excess iodine exposure indicated severe deterioration of seminiferous tubular surface architecture. Significant increase in spermatozoal DNA fragmentation and apoptotic sperms were found by acridine orange and Annexin V, respectively, however the plasma membrane integrity/viability was decreased as evident by propidium iodide staining in various incremental doses and durations under iodine excess. The study reveals that excess iodine could cause apoptosis of spermatozoal cells by inducing ROS that ultimately affects male fertility potential.
Improved Risk Assessment by Integrating Molecular and Clinicopathological Factors in Early-stage Endometrial Cancer—Combined Analysis of the PORTEC Cohorts

Purpose: Recommendations for adjuvant treatment for women with early-stage endometrial carcinoma are based on clinicopathologic features. Comprehensive genomic characterization defined four subgroups: p53-mutant, microsatellite instability (MSI), POLE-mutant, and no specific molecular profile (NSMP). We aimed to confirm the prognostic capacity of these subgroups in large randomized trial populations, investigate potential other prognostic classifiers, and integrate these into an integrated molecular risk assessment guiding adjuvant therapy. Experimental Design: Analysis of MSI, hotspot mutations in 14 genes including POLE, protein expression of p53, ARID1a, β-catenin, L1CAM, PTEN, ER, and PR was undertaken on 947 available early-stage endometrioid endometrial carcinomas from the PORTEC-1 and -2 trials, mostly high-intermediate risk (n = 614). Prognostic value was determined using univariable and multivariable Cox proportional hazard models. AUCs of different risk stratification models were compared. Results: Molecular analyses were feasible in >96% of the patients and confirmed the four molecular subgroups: p53-mutant (9%), MSI (26%), POLE-mutant (6%), and NSMP (59%). Integration of prognostic molecular alterations with established clinicopathologic factors resulted in a stronger model with improved risk prognostication. Approximately 15% of high-intermediate risk patients had unfavorable features (substantial lymphovascular space invasion, p53-mutant, and/or >10% L1CAM), 50% favorable features (POLE-mutant, NSMP being microsatellite stable, and CTNNB1 wild-type), and 35% intermediate features (MSI or CTNNB1-mutant). Conclusions: Integrating clinicopathologic and molecular factors improves the risk assessment of patients with early-stage endometrial carcinoma. Assessment of this integrated risk profile is feasible in daily practice, and holds promise to reduce both overtreatment and undertreatment. Clin Cancer Res; 22(16); 4215–24. ©2016 AACR.
ANViL: Advanced Virtualization for Modern Non-Volatile Memory Devices

We present a new form of storage virtualization based on block-level address remapping. By allowing the host system to manipulate this address map with a set of three simple operations (clone, move, and delete), we enable a variety of useful features and optimizations to be readily implemented, including snapshots, deduplication, and single-write journaling. We present a prototype implementation called Project ANViL and demonstrate its utility with a set of case studies.
Managing Information Technology in Small Business: Challenges and Solutions

In many countries, small businesses comprise over 95% of the proportion of private businesses and approximately half of the private workforce, with information technology being used in over 90% of these businesses. As a result, governments worldwide are placing increasing importance upon the success of small business entrepreneurs and are providing increased resources to support this emphasis. Managing Information Technology in Small Business: Challenges and Solutions presents research in areas such as IT performance, electronic commerce, internet adoption, and IT planning methodologies and focuses on how these areas impact small businesses.
How to reason with OWL in a logic programming system

Logic programming has always been a major ontology modeling paradigm, and is frequently being used in large research projects and industrial applications, e.g., by means of the F-Logic reasoning engine OntoBroker or the TRIPLE query, inference, and transformation language and system. At the same time, the Web Ontology Language OWL has been recommended by the W3C for modeling ontologies for the Web. Naturally, it is desirable to investigate the interoperability between both paradigms. In this paper, we do so by studying an expressive fragment of OWL DL for which reasoning can be reduced to the evaluation of Horn logic programs. Building on the KAON2 algorithms for transforming OWL DL into disjunctive Datalog, we give a detailed account of how and to what extent OWL DL can be employed in standard logic programming systems. En route, we derive a novel, simplified characterization of the supported fragment of OWL DL
The Hog1 SAPK controls the Rtg1/Rtg3 transcriptional complex activity by multiple regulatory mechanisms

The retrograde (RTG) pathway transcription factors Rtg1 and Rtg3 are shown to be targets of the Hog1 stress-activated protein kinase (SAPK). Hog1 acts on the RTG complex at multiple levels to mediate gene expression upon stress. The SAPK is required for the nuclear accumulation of the complex, the recruitment of the complex at RTG-responsive promoters, and the regulation of Rtg3 transcriptional activity.
Combining Collective Classification and Link Prediction

The problems of object classification (labeling the nodes of a graph) and link prediction (predicting the links in a graph) have been largely studied independently. Commonly, object classification is performed assuming a complete set of known links and link prediction is done assuming a fully observed set of node attributes. In most real world domains, however, attributes and links are often missing or incorrect. Object classification is not provided with all the links relevant to correct classification and link prediction is not provided all the labels needed for accurate link prediction. In this paper, we propose an approach that addresses these two problems by interleaving object classification and link prediction in a collective algorithm. We investigate empirically the conditions under which an integrated approach to object classification and link prediction improves performance, and find that performance improves over a wide range of network types, and algorithm settings.
Unravelling the Complexity: Understanding the Challenges of Reinforcement Learning

: After an extensive research and exploration of supervised, unsupervised and semi-supervised machine learning algorithms, researchers across the numerous application domains of machine learning are now looking to implement reinforcement learning techniques as they promise a realization of more human-like intelligence in machines. This paper presents a comprehensive body of knowledge about the complexities and challenges that researchers might face while developing reinforcement learning models as solutions for real-life problems. Also, some recommendations have been made in order to assist effective implementation of reinforcement learning algorithms
Clinical efficacy and safety of lactulose for minimal hepatic encephalopathy: a meta-analysis

Objective To evaluate the clinical efficacy of lactulose in patients with minimal hepatic encephalopathy (MHE). Methods Randomized controlled trials (RCTs) comparing lactulose with placebo or with no intervention in the management of MHE that were conducted from January 1990 to July 2011 were searched from MEDLINE, EMBASE, SCI, Cochrane Controlled Trials Register, and China Biological Medicine Database. Studies with a Jadad score higher than 3 were included in the meta-analysis and evaluated using RevMan5.0 software for relative risk (RR) or weighted mean difference (WMD) with 95% confidence intervals (95% CI). Sensitivity analysis was performed on the ethnical differences and quality of the trials. Publication bias was observed using an inverted funnel plot. Results Nine studies with 434 patients were included in the meta-analysis. Compared with placebo or no intervention, lactulose significantly reduced the risk of no improvement in neuropsychological tests (RR: 0.52, 95% CI: 0.44–0.62, P<0.00001), the time required for the completion of the number connection test-A (WMD: −26.95, 95% CI: −37.81 to −16.10, P<0.00001), and the mean number of abnormal neuropsychological tests (WMD: −1.76, 95% CI: −1.96 to −1.56, P<0.00001). Furthermore, the meta-analysis also showed that lactulose prevented the progression to overt hepatic encephalopathy (RR: 0.17, 95% CI: 0.06–0.52, P=0.002), reduced blood ammonia levels (WMD: −9.89 µmol/l, 95% CI: −11.01 to −8.77 µmol/l, P<0.00001), and improve health-related quality of life (WMD: −6.05, 95% CI: −6.30 to −5.20, P<0.00001). However, no significant difference was observed in the mortality of patients with MHE (RR: 0.75, 95% CI: 0.21–2.72, P=0.66), and lactulose significantly increased the incidence of diarrhea (RR: 4.38, 95% CI: 1.35–14.25, P=0.01). Conclusion Lactulose has significant beneficial effects for patients with MHE compared with placebo or no intervention.
Reducing SAR and enhancing cerebral signal‐to‐noise ratio with high permittivity padding at 3 T

Previous works have shown that placement of a high‐dielectric pad can improve image intensity in a region adjacent to the pad, or that placement of dielectric pads around a large surface of the head can improve image homogeneity on an entire plane through the head in high‐field MRI. Here, experimental results show that use of high‐dielectric pads around the human head can reduce the required input radiofrequency power by 50% while enhancing image signal‐to‐noise ratio by 20–40% throughout the cerebrum at 3 T. Thus, dielectric pads may be used to provide a relatively simple and low‐cost method for improving quality and safety of MRI in a variety of applications at 3 T. Magn Reson Med, 2011. © 2010 Wiley‐Liss, Inc.