ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Wireless Architectures for Heterogeneous Sensing in Smart Home Applications: Concepts and Real Implementation

Application of wireless technologies in the smart home is dealt with by pointing out advantages and limitations of available approaches for the solution of heterogeneous and coexisting problems related to the distributed monitoring of the home and the inhabitants. Some hot challenges facing the exploitation of noninvasive wireless devices for user behavior monitoring are then addressed and the application fields of smart power management and elderly people monitoring are chosen as representative cases where the estimation of user activities improves the potential of location-aware services in the smart home. The problem of user localization is considered with great care to minimize the invasiveness of the monitoring system. Wireless architectures are reviewed and discussed as flexible and transparent tools toward the paradigm of a totally automatic/autonomic environment. With respect to available state-of-the-art solutions, our proposed architecture is based also on existing wireless devices and exploits, in an opportunistic way, the characteristics of wireless signals to estimate the presence, the movements, and the behaviors of inhabitants, reducing the system complexity and costs. Selected and representative examples from real implementations are presented to give some insight on state-of-the-art solutions also envisaging possible future trends.
SNPSFuzzer: A Fast Greybox Fuzzer for Stateful Network Protocols Using Snapshots

Greybox fuzzing has been widely used in stateless programs and has achieved great success. However, most state-of-the-art greybox fuzzers have slow speed and shallow state depth coverage in fuzzing stateful network protocol programs, which are able to remember and store the details of interactions. The existing greybox fuzzers for network protocol programs first send a series of well-defined prefix sequences of input messages and then send mutated messages to test the target state of a stateful network protocol. This process leads to a high time cost. In this paper, we propose SNPSFuzzer, a fast greybox fuzzer for stateful network protocols using snapshots. SNPSFuzzer dumps the context information when the network protocol program is in a specific state and restores it when the state needs to be fuzzed. Furthermore, we design a message chain analysis algorithm to explore more and deeper network protocol states. Our evaluation shows that compared with the state-of-the-art network protocol greybox fuzzer AFLNET, SNPSFuzzer improves the message processing speed of network protocol fuzzing by 70.7% and increases the path coverage by 20.9% on average within 24 hours. Moreover, SNPSFuzzer exposes a previously unreported vulnerability in the program Tinydtls.
Dynamic interplay and function of multiple noncoding genes governing X chromosome inactivation

There is increasing evidence for the emergence of long noncoding RNAs (lncRNAs) as important components, especially in the regulation of gene expression. In the event of X chromosome inactivation, robust epigenetic marks are established in a long noncoding Xist RNA-dependent manner, giving rise to a distinct epigenetic landscape on the inactive X chromosome (Xi). The X inactivation center (Xic) is essential for induction of X chromosome inactivation and harbors two topologically associated domains (TADs) to regulate monoallelic Xist expression: one at the noncoding Xist gene and its upstream region, and the other at the antisense Tsix and its upstream region. The monoallelic expression of Xist is tightly regulated by these two functionally distinct TADs as well as their constituting lncRNAs and proteins. In this review, we summarize recent updates in our knowledge of lncRNAs found at the Xic and discuss their overall mechanisms of action. We also discuss our current understanding of the molecular mechanism behind Xist RNA-mediated induction of the repressive epigenetic landscape at the Xi. This article is part of a Special Issue entitled: Clues to long noncoding RNA taxonomy1, edited by Dr. Tetsuro Hirose and Dr. Shinichi Nakagawa.
Decentralized Proximal Gradient Algorithms With Linear Convergence Rates

This article studies a class of nonsmooth decentralized multiagent optimization problems where the agents aim at minimizing a sum of local strongly-convex smooth components plus a common nonsmooth term. We propose a general primal-dual algorithmic framework that unifies many existing state-of-the-art algorithms. We establish linear convergence of the proposed method to the exact minimizer in the presence of the nonsmooth term. Moreover, for the more general class of problems with agent specific nonsmooth terms, we show that linear convergence cannot be achieved (in the worst case) for the class of algorithms that uses the gradients and the proximal mappings of the smooth and nonsmooth parts, respectively. We further provide a numerical counterexample that shows how some state-of-the-art algorithms fail to converge linearly for strongly convex objectives and different local non smooth terms.
A meta-analysis of simulator sickness as a function of simulator fidelity

Driving simulators are an increasingly important tool to develop vehicle functionalities and to study driver or passenger responses. A major hindrance to the use and validity of such studies is Simulator Sickness (SS). Several studies have suggested a positive relation between improvements in simulator fidelity and the likelihood of sickness. We hypothesized that this relation only holds true for static (fixed-base) simulators, and that increased fidelity in fact reduces simulator sickness in dynamic (moving-base) simulators. We performed a meta-analysis investigating the relation between sickness and fidelity in static and dynamic systems. A literature search yielded a total of 41 simulator studies that varied aspects of mechanical and/or visual fidelity and assessed SS for the same driving conditions and the same or equivalent participant groups. Evaluation of a model synthesizing the findings of these studies indicates that SS decreases with visual fidelity, and suggests that this effect may be negated for static simulators. The results of the modeling efforts thereby provide some support for the hypothesis that increased fidelity can reduce SS in dynamic simulators. Based on the evaluation of the literature we also note particular shortcomings and gaps in available research. Finally, we make recommendations for specific experiments that may fill these gaps and allow definitive conclusions on the role of simulator fidelity in SS.
Forest based on Interval Transformation (FIT): A time series classifier with adaptive features

Time series classification (TSC) is an important task in time series data mining and has attracted a lot of research attention. Most TSC algorithms aim to achieve high classification accuracy while reducing the computational complexity. Currently, Time Series Combination of Heterogeneous and Integrated Embedding Forest (TS-CHIEF) is considered to be one of the state-of-the-art TSC algorithms. However, compared with fast algorithms such as Time Series Forest (TSF), TS-CHIEF still has high computation cost. On the premise that the TSF algorithm is fast, we propose a new TSC algorithm, Forest based on Interval Transformation (called FIT), which takes into account both accuracy and efficiency. FIT uses cross-validation to select appropriate transformation series and corresponding interval features, and adaptively converts the interval features of each series in the process of formal training. Subsequently, the transformed feature set is combined with the random forest training FIT model. We evaluate the performance of FIT on 85 UCR time series classification datasets. The experimental results demonstrate that FIT can achieve better accuracy while maintaining high efficiency compared with the state-of-the-art methods.
Crack Detection in Paintings Using Convolutional Neural Networks

The accurate detection of cracks in paintings, which generally portray rich and varying content, is a challenging task. Traditional crack detection methods are often lacking on recent acquisitions of paintings as they are poorly adapted to high-resolutions and do not make use of the other imaging modalities often at hand. Furthermore, many paintings portray a complex or cluttered composition, significantly complicating a precise detection of cracks when using only photographic material. In this paper, we propose a fast crack detection algorithm based on deep convolutional neural networks (CNN) that is capable of combining several imaging modalities, such as regular photographs, infrared photography and X-Ray images. Moreover, we propose an efficient solution to improve the CNN-based localization of the actual crack boundaries and extend the CNN architecture such that areas where it makes little sense to run expensive learning models are ignored. This allows us to process large resolution scans of paintings more efficiently. The proposed on-line method is capable of continuously learning from newly acquired visual data, thus further improving classification results as more data becomes available. A case study on multimodal acquisitions of the <italic>Ghent Altarpiece</italic>, taken during the currently ongoing conservation-restoration treatment, shows improvements over the state-of-the-art in crack detection methods and demonstrates the potential of our proposed method in assisting art conservators.
Face alignment using a 3D deeply-initialized ensemble of regression tress

Face alignment algorithms locate a set of landmark points in images of faces taken in unrestricted situations. State-of-the-art approaches typically fail or lose accuracy in the presence of occlusions, strong deformations, large pose variations and ambiguous configurations. In this paper we present 3DDE, a robust and efficient face alignment algorithm based on a coarse-to-fine cascade of ensembles of regression trees. It is initialized by robustly fitting a 3D face model to the probability maps produced by a convolutional neural network. With this initialization we address self-occlusions and large face rotations. Further, the regressor implicitly imposes a prior face shape on the solution, addressing occlusions and ambiguous face configurations. Its coarse-to-fine structure tackles the combinatorial explosion of parts deformation. In the experiments performed, 3DDE improves the state-of-the-art in 300W, COFW, AFLW and WFLW data sets. Finally, we perform cross-dataset experiments that reveal the existence of a significant data set bias in these benchmarks.
Association analysis of PLIN2 gene polymorphisms and lambing performance in Qianbei Ma goats

Qianbei Ma goats are one of the three most valued local goat breeds in Guizhou, China; furthermore, it has lower litter size performance. The purpose of this study was to explore the correlation between SNP (single-nucleotide polymorphisms) of PLIN2 gene and lambing performance. The bioinformatics analysis, DNA sequencing, RT-qPCR and correlation analysis methods were used to analyse the evolutionary relationship of PLIN2 protein in 13 species, to detect the expression pattern of PLIN2 gene in the gonad axis of Qianbei sheep, to explore the dominant genotype of PLIN2 related to lambing traits and to screen molecular markers related to lambing performance to guide the breeding of Qianbei Ma goats. Results showed that the Qianbei Ma goat PLIN2 protein had the closest genetic relationship with sheep and the furthest from mice, there were significant or extremely significant differences in the expression levels of the PLIN2 gene in the gonadal axis of the mothers of single- and multi-lamb groups. Compared with the reference sequence, four SNPs were found, which were g.1006 C → A and g.1171 A → G in the first and second intron regions of the PLIN2 gene, g.8514 C → T in the exon 8 region and g.9122 A → T in the 3'UTR. The correlation analysis showed that g.1006 C → A, g.8514 C → T and g.9122 A → T had significant indigenous effects on the lambing performance of Qianbei Ma goats (p < .05). The number of third births for diploid H2H5 was significantly higher than that of diploid H1H2, and the number of first to third births for diploid H2H5 was large and stable. The results showed that PLIN2 gene could be used as a candidate gene related to lambing traits of Qianbei Ma goat.
Effects of the learning tasks and the leadership profile of the coach on the perception of decision-making in young

The objective of the research was to investigate the effects of the learning tasks and leadership profile of the coach on the subjective perception of decision-making. In total, 910 Brazilian adolescents (14.00 ± 1.8 years) and 57 Brazilian coaches (45.57 ± 7.25 years) were included in the study, participants of a sports program in 37 public schools in the city of Curitiba in the state of Paraná. Ordinal logistic regression was used to verify the independent associations between the variables and the tertiles of the scores for each decision-making characteristic. The small-sized game learning tasks were positively associated with commitment to decision-making learning (CR = 1.21, 95% CI = 1.01-1.49). Positive associations were also found between the coaches' leadership profile and decision-making competence.
A new Aurignacian engraving from Abri Blanchard, France: Implications for understanding Aurignacian graphic expression in Western and Central Europe

In the excitement of the widely publicized new finds of Aurignacian art from Chauvet, from the Swabian Jura and from as far afield as Pestera Coliboaia in Romania, it has almost been forgotten that a rich corpus of Aurignacian wall painting, engraving and bas-relief sculpture had been recognized and studied before World War I in the Vezere Valley of SW France. Scientific knowledge of the chronological and cultural context of that early-discovered graphic record has been limited by the crude archaeological methods of that pioneering era, and the loss and dispersal of many of the works discovered. In 2011, we launched new excavations and a re-analysis of one of the key sites for such early discoveries, the collapsed rock shelter of Abri Blanchard. In 2012, we discovered in situ a limestone slab engraved with a complex composition combining an aurochs and dozens of aligned punctuations. This new find, recovered by modern methods and dated by molecular filtration and Hydroxyproline 14C, provides new information on the context and dating of Aurignacian graphic imagery in SW France and its relationship to that of other regions. The support is not a fragment of collapsed shelter ceiling and is situated in the midst of quotidian occupational debris. The image shows significant technical and thematic similarities to Chauvet that are reinforced by our reanalysis of engraved slabs from the older excavations at Blanchard. The aligned punctuations find their counterparts at Chauvet, in the south German sites and on several other objects from Blanchard and surrounding Aurignacian sites. In sum, we argue that dispersing Aurignacian groups show a broad commonality in graphic expression against which a certain number of more regionalized characteristics stand out, a pattern that fits well with social geography models that focus on the material construction of identity at regional, group and individual levels. (C) 2016 Elsevier Ltd and INQUA. All rights reserved.
A comprehensive survey of the approaches for pathway analysis using multi-omics data integration

Pathway analysis has been widely used to detect pathways and functions associated with complex disease phenotypes. The proliferation of this approach is due to better interpretability of its results and its higher statistical power compared with the gene-level statistics. A plethora of pathway analysis methods that utilize multi-omics setup, rather than just transcriptomics or proteomics, have recently been developed to discover novel pathways and biomarkers. Since multi-omics gives multiple views into the same problem, different approaches are employed in aggregating these views into a comprehensive biological context. As a result, a variety of novel hypotheses regarding disease ideation and treatment targets can be formulated. In this article, we review 32 such pathway analysis methods developed for multi-omics and multi-cohort data. We discuss their availability and implementation, assumptions, supported omics types and databases, pathway analysis techniques and integration strategies. A comprehensive assessment of each method's practicality, and a thorough discussion of the strengths and drawbacks of each technique will be provided. The main objective of this survey is to provide a thorough examination of existing methods to assist potential users and researchers in selecting suitable tools for their data and analysis purposes, while highlighting outstanding challenges in the field that remain to be addressed for future development.
Ectopic breast carcinoma

Carcinoma of the accessory breast tissue (CABT) is an extremely rare occurrence, representing 0.3% of all breast malignancies. A 65-year-old, postmenopausal woman was referred to our Breast Clinic complaining of a palpable, growing, and painful mass in her right axilla. Physical examination revealed a palpable tender mass, approximately 3 cm in size, visibly infiltrating the overlying skin area, while physical examination of the breast revealed no palpable lesions. Core biopsy of the mass was promptly scheduled, and the histological report came back positive for Nottingham Grade II NST invasive carcinoma of the breast. The patient underwent breast-conserving surgery and concomitant axillary lymph node dissection (ALND) for removal of the malignant mass. Care was taken to preserve the axillary vein and the long thoracic nerve. Closure of the axillary incision required mobilization of skin flaps to ensure optimal cosmetic results. Despite the ectopic breast tissue being a largely benign and infrequent occurrence, the breast surgeon must remain vigilant for the possibility of CABT development. At any rate, further epidemiological studies incorporating as many patients as possible are required in order to formulate recommendations on the management and prognosis of CABT. Until such guidelines exist, excision of the carcinoma, along with ALND performance, is a reasonable and justified approach to the surgical treatment of CABT.
The interplay of race/ethnicity and education in fertility patterns

This study examines the interplay between race/ethnicity and educational attainment in shaping completed fertility in the United States for women born 1961-80. Using data from the National Survey of Family Growth, 2006-17, we apply multilevel, multiprocess hazard models to account for unobserved heterogeneity and to estimate (1) cohort total fertility rates, (2) parity progression ratios, and (3) parity-specific fertility timing, for non-Hispanic white, non-Hispanic Black, and Hispanic women by educational attainment. We find that compared with their white counterparts, fertility was higher among Black and Hispanic women with less than high school education. However, among college-educated women, fertility levels were lowest among Black women and highest among Hispanic women. The difference in fertility between college-educated Black and white women is driven mainly by the smaller proportion of Black mothers having second births. We find little evidence that the observed racial/ethnic disparities in fertility levels by educational attainment are driven by differences in fertility timing.
Progressive Multiscale Consistent Network for Multiclass Fundus Lesion Segmentation

Effectively integrating multi-scale information is of considerable significance for the challenging multi-class segmentation of fundus lesions because different lesions vary significantly in scales and shapes. Several methods have been proposed to successfully handle the multi-scale object segmentation. However, two issues are not considered in previous studies. The first is the lack of interaction between adjacent feature levels, and this will lead to the deviation of high-level features from low-level features and the loss of detailed cues. The second is the conflict between the low-level and high-level features, this occurs because they learn different scales of features, thereby confusing the model and decreasing the accuracy of the final prediction. In this paper, we propose a progressive multi-scale consistent network (PMCNet) that integrates the proposed progressive feature fusion (PFF) block and dynamic attention block (DAB) to address the aforementioned issues. Specifically, PFF block progressively integrates multi-scale features from adjacent encoding layers, facilitating feature learning of each layer by aggregating fine-grained details and high-level semantics. As features at different scales should be consistent, DAB is designed to dynamically learn the attentive cues from the fused features at different scales, thus aiming to smooth the essential conflicts existing in multi-scale features. The two proposed PFF and DAB blocks can be integrated with the off-the-shelf backbone networks to address the two issues of multi-scale and feature inconsistency in the multi-class segmentation of fundus lesions, which will produce better feature representation in the feature space. Experimental results on three public datasets indicate that the proposed method is more effective than recent state-of-the-art methods.
The applicability of repeat photography in rock art conservation: a case study of mixed methods in the Arkansan Ozarks

Detailed scientific analyses of rock decay and site stability can aid rock art conservation but cultural sensitivity often limits the use of traditional methods, which often require rock samples or other invasive procedures. The need for intuitive non-invasive rock art analysis led to the creation of the Rock Art Stability Index (RASI). While RASI is a validated rock art assessment tool, a primarily quantitative approach can sometimes overlook the aesthetic component of rock art - a characteristic critical to the presentation and perceived value of the resource. Therefore, this research examined the applicability of repeat photography, or rephotography, in addition to RASI providing a more comprehensive approach. This mixed method approach was employed at three different rock art sites in the Arkansan Ozarks of varying lithologies, geographic characteristics, rock art type, and management policies. Results suggest that RASI allows for more consistent and reliable assessments, as rephotography has more restrictions and limitations. Nevertheless, when available, rephotography proved to be an immensely useful addition to quantitative research techniques, such as RASI, to provide heritage management with a more holistic evaluation addressing both geologic stability and changes in visual quality over time.
Attentional biases in human anxiety

Across clinical and subclinical samples, anxiety has been associated with increased attentional capture by cues signaling danger. Various cognitive models attribute the onset and maintenance of anxiety symptoms to maladaptive selective information processing. In this brief review, we 1) describe the evidence for the relations between anxiety and attention bias toward threat, 2) discuss the neurobiology of anxiety-related differences in threat bias, 3) summarize work investigating the developmental origins of attention bias toward threat, and 4) examine efforts to translate threat bias research into clinical intervention. Future directions in each area are discussed, including the use of novel analytic approaches improving characterization of threat-processing-related brain networks, clarifying the role of cognitive control in the development of attention bias toward threat, and the need for larger, well-controlled randomized clinical trials examining moderators and mediators of treatment response. Ultimately, this work has important implications for understanding the etiology of and for intervening on anxiety difficulties among children and adults.