Miracle-dz/UniFAR-FTUs huggingface dataset:
Microbiome Profiles in Periodontitis in Relation to Host and Disease Characteristics

Periodontitis is an inflammatory condition that affects the supporting tissues surrounding teeth. The occurrence of periodontitis is associated with shifts in the structure of the communities that inhabit the gingival sulcus. Although great inter-subject variability in the subgingival microbiome has been observed in subjects with periodontitis, it is unclear whether distinct community types exist and if differences in microbial signatures correlate with host characteristics or with the variable clinical presentations of periodontitis. Therefore, in this study we explored the existence of different community types in periodontitis and their relationship with host demographic, medical and disease-related clinical characteristics. Clustering analyses of microbial abundance profiles suggested two types of communities (A and B) existed in the 34 subjects with periodontitis evaluated. Type B communities harbored greater proportions of certain periodontitis-associated taxa, including species historically associated with the disease, such as Porphyromonas gingivalis, Tannerella forsythia and Treponema denticola, and taxa recently linked to periodontitis. In contrast, subjects with type A communities had increased proportions of different periodontitis-associated species, and were also enriched for health-associated species and core taxa (those equally prevalent in health and periodontitis). Periodontitis subgingival clusters were not associated with demographic, medical or disease-specific clinical parameters other than periodontitis extent (proportion of sites affected), which positively correlated with the total proportion of cluster B signature taxa. In conclusion, two types of microbial communities were detected in subjects with periodontitis. Host demographics and underlying medical conditions did not correlate with these profiles, which instead appeared to be related to periodontitis extent, with type B communities present in more widespread disease cases. The two identified periodontitis profiles may represent distinct dysbiotic processes potentially requiring community-tailored therapeutic interventions.
ENF analysis on recaptured audio recordings

Electric Network Frequency (ENF) based forensic analysis is a promising tool for timestamp authentication and forgery detection in such multimedia recordings as audios and videos. ENF signal is embedded in an audio recording due to electromagnetic interference from the power lines. The time of creation of a multimedia recording can be determined by comparing the ENF signal embedded in the recording with a reference ENF database collected from the power grid. In this paper, we conduct a study of the effect of recapturing of audio recordings on the ENF embedding. We demonstrate that recaptured audio recordings pick up two ENF signals: the content ENF signal which is inherited from the original audio recording; and the recapturing ENF signal which is embedded from the recapturing process. Conventional ENF signal extraction techniques on such recordings may fail when the two ENF signals are at the same nominal value. A decorrelation algorithm is proposed to extract the content ENF signal and the recapturing ENF signal. The experimental results show the effectiveness of the proposed method in the estimation of both the ENF signals.
The Effect of Altitude on Intraocular Pressure in Vitrectomized Eyes with Sulfur Hexafluoride Tamponade by the Friedenwald Method: Rabbit Animal Model

The aim of this study is to assess the change in intraocular pressure after a road trip, in eyes with different levels of filling with gas tamponade. Five rabbit eyes were subject to pars plana vitrectomy and gas tamponade (filling percentage: 25%, 50%, and 100% of nonexpansile SF6, 100% saline solution, and 100% room air). A sixth eye was injected with 0.35 cc of undiluted SF6 without vitrectomy. Guided by global positioning system, they were driven to the highest point of the highway connecting Mexico City with Puebla city and back, stopping every 300 m to assess intraocular pressure. The rabbit's scleral rigidity and estimation for human eyes were done by using the Friedenwald nomogram. Maximum altitude was 3209 m (Δ949 m). There were significant differences in intraocular pressure on the rabbit eyes filled with SF6 at 100%, 50%, 25%, and 100% room air. Per every 100 m of altitude rise, the intraocular pressure increased by 1.53, 1.0046, 0.971, and 0.97 mmHg, respectively. Using the human Friedenwald rigidity coefficient, the human eye estimate for intraocular pressure change was 2.1, 1.8, 1.4, and 1.1 mmHg per every 100 m of attitude rise. Altitude changes have a significant impact on intraocular pressure. The final effect depends on the percentage of vitreous cavity fill and scleral rigidity.
Semantic visual search with feature space reduction

High dimensional visual features and multimodal based search systems are popular due to their visual retrieval accuracy. But high dimensionality and multimodality negatively affect efficiency and computational complexity. This paper brings the performance advantages of low dimensional visual feature called Compacted Dither Pattern Code (CDPC). CDPC is accompanied with texel properties, spatial colour arrangements, high compactness, and robust feature representation. CDPC feature uses a low computational cost in visual feature extraction which is illustrated using four abstract stages. This feature has proven its performances in our experimental study. In our experiment, CDPC was used alone as a syntactic visual feature for semantic visual depiction and retrieval. Ten irregular shapes based visual concepts were used in visual description. The average precision results generated from ranked retrieval set were used as a metric in evaluation. The retrieval result based on Trecvid 2005 dataset is presented compared to well known low dimensional and high dimensional visual features. Results show the balanced made by CDPC with Bhattacharyya classification in between retrieval accuracy and visual feature space reduction. Competitive advantages of this method over state of art systems are also technically described.
Structural Investigation of Heteroepitaxial 3C-SiC Grown on 4H-SiC Substrates

3C-SiC epilayers grown on Si-face nominally on-axis 4H-SiC substrate are characterized with X-ray diffraction techniques. The aim was to investigate if these 3C-SiC epilayers were grown by single domain growth. The results show that all samples start by having several nucleation centers all over the substrate surface and the growth continues with two domain formations. As the growth proceeds one domain overtakes the growth and single domain crystal growth occurs. This single domain was further investigated and the results show that it seems to contain many sub-domains with same lattice constant but slightly tilted.
A low-dimensional representation for individual head geometries

In the estimation of individual head geometries for source localization and electrical stimulation in neuroelectric investigations and application, mostly complex geometrical models are directly extracted from anatomical images. We present a novel method that uses a dimensionality reduction from thousands down to the range of tens of parameters to successfully represent an individual 4-shell Boundary Element Method (BEM) head model, which can successively be fitted to any kind of data from an individual head (e.g. headshape, impedances) and then used for individual head model creation. The results show, that around 15 – 20 components can lead to satisfactory results.
On spectral efficiency of asynchronous OFDM/FBMC based cellular networks

The growing demand for wireless communication makes it important to determine the capacity limits of the different multicarrier systems. In this paper, we address the impact of imperfect inter-cell synchronization on the average capacity of orthogonal frequency division multiplexing (OFDM) and filter bank based multicarrier (FBMC) multi-cellular networks. Based on computing the moment generating functions of the asynchronous interference power, a simple new expression for the exact evaluation of the average capacity is derived considering the frequency correlation fading between adjacent interfering subcarriers.
Mobile robot Path planning based on Behavior Information Potential Field in unknown Environments

a path planning method based on the new concept of behavior information potential field (BIPF) is proposed in this paper. The basic idea is to build an imaginary field represent the behaviors of robot. The BIPF is a crucial factor for path planning. The BIPF method uses a 2D Cartesian histogram grid as an imaginary field model which is updated continuously by the actions of robot. The robot tends to observe the region in which the BIPF value less than others. An optimality punishment function based on BIPF is given by this paper. We use this function to choice the optimal action as control vector. Experimental result shows the performance of BIPF method in this paper
Statistical characterization of detail preservation

A novel method of quantifying the level of detail preservation ability of digital filters is proposed. The method assumes only the input distribution of the filter and estimates how much the filter changes the signal. The change is measured by the expectation of the absolute difference between the input and output signal. The method is applicable for many filters and input distributions. As an example case, the formulas for the expectation of the absolute difference for weighted order statistic filters with the uniform and Laplacian (biexponential) input distributions are derived. Finally, the design of weighted order statistic filters using supervised learning is studied. The learning method uses the detail preservation measure as a design criterion to obtain filters with different levels of detail preservation.
Regular XPath: Constraints, Query Containment and View-Based Answering for XML Documents

In this paper we consider a powerful mechanism, called Regular XPath, for expressing queries and constraints over XML data, including DTDs and existential path constraints and their negation. Regular XPath extends XPath with binary relations over XML nodes specified by means two-way regular path queries. Our first contribution deals with checking satisfiability of Regular XPath constraints. While this problem could be reduced in terms of reasoning in repeat converse deterministic PDL, a well-known variant Propositional Dynamic Logic (PDL), the resulting technique would be of little practical use, due to the notorious difficulty of implementing efficient reasoners for such a logic. We therefore propose a direct algorithm for Regular XPath constraints satisfiability, based on checking emptiness of two way alternating automata on finite trees. We show how this algorithm can be implemented symbolically, by using Binary Decision Diagrams (BDDs) as the underlying data structure, which can be significantly more efficient than explicit graph-based algorithms. We then move to query containment and view based query answering for Regular XPath, and show that both problems can be reduced to checking satisfiability of Regular XPath constraints, thus allowing for taking advantage of the techniques developed for constraints
Social structure simulation and inference using artificial intelligence techniques

The study of complex social and technological systems, such as organizations, requires a sophisticated approach that accounts for the underlying psychological and sociological principles, communication patterns and the technologies within these systems. Social Network Analysis and link analysis have since inception operated on the cutting edge bringing together mathematical analysis of social structures and qualitative reasoning and interpretation. As available computing power grew, social network-based models have become not only an analysis tool, but also a methodology for building new theories of social behaviour and organizational evolution, frequently through the creation of simulation models. This work examines the past approaches of creating Social Network-based semantically consistent and interpretable models of social structure and social networks, as well as social simulation tools. I propose the creation of a multi-theory, multi-level simulation model of social structure that relies on social network theory and Artificial Intelligence algorithms. I further propose the creation of a robust and scalable social structure semantic that facilitates interpretable reasoning about evolution of social structure.
GEARSHIFT: Guaranteeing availability requirements in SLAs using hybrid fault tolerance

The dependability of ICT systems is vital for today's society. However, operational systems are not fault free. Providers and customers have to define clear availability requirements and penalties on the delivered services by using SLAs. Fulfilling the stipulated availability may be expensive. The lack of mechanisms that allow a fine control of the SLA risk may lead to over-dimension the provided resources. Therefore, a relevant question for ICT service providers is: How to guarantee the SLA availability in a cost efficient way? This paper studies how to combine different fault tolerant techniques with different costs and properties, in order to economically fulfill a given SLA requirement. GEARSHIFT is a mechanism that enables ICT providers to set the fault tolerance technique (gear ratio) needed, depending on the current service conditions and requirements. We illustrate how to use the proposed model in a backbone network scenario, using measurements from a production national network. Finally, we show that the total costs of delivering an ICT service follow a simple convex function, which allows an easy selection of the optimal risk by tuning properly the combination of fault tolerant techniques.
New adaptive beamforming algorithm employing forward/backward averaging and signal enhancement schemes

We introduce the new adaptive beamforming algorithm which improves the performance of an adaptive antenna array system through a forward/backward averaging scheme of the post-correlation signal vector and a signal enhancement scheme using Hermitian Toeplitzation of an array covariance matrix in DS/CDMA. A forward/backward averaging scheme decorrelates the received correlated signal after despreading in a matched filter and the Hermitian Toeplitzation scheme enhances the performance of the received signal by removing the undesired effect obtained from an array covariance matrix estimation. It is shown through simulation results that the performance of the proposed algorithm is very superior to that of the conventional Wiener maximal ratio combining (MRC) algorithm.
A Backward Algorithm for the Multiprocessor Online Feasibility of Sporadic Tasks

The online feasibility problem (for a set of sporadic tasks) asks whether there is a scheduler that always prevents deadline misses (if any), whatever the sequence of job releases, which is a priori unknown to the scheduler. In the multiprocessor setting, this problem is notoriously difficult. The only exact test for this problem has been proposed by Bonifaci and Marchetti-Spaccamela: it consists in modelling all the possible behaviours of the scheduler and of the tasks as a graph, and to interpret this graph as a game between the tasks and the scheduler, which are seen as antagonistic players. Then, computing a correct scheduler is equivalent to finding a winning strategy for the 'scheduler player', whose objective in the game is to avoid deadline misses. In practice, however this approach is limited by the intractable size of the graph. In this work, we consider the classical attractor algorithm to solve such games, and introduce antichain techniques to optimise its performance in practice and overcome the huge size of the game graph. These techniques are inspired from results from the formal methods community, and exploit the specific structure of the feasibility problem. We demonstrate empirically that our approach allows to dramatically improve the performance of the game solving algorithm.
Impact of Using Multi-Packet Reception on Performance in Delay Tolerant Networks

Delay Tolerant Network (DTN) is a kind of networks that have no fixed path between nodes due to lack of continuous network connectivity. To achieve successful communication and to improve performance in DTN is always a challenge work. Most existing works are based on designing high efficient routing protocols and have not emphasized the MAC layer as well as new technologies available in the Physical layer which may influence the performance for whole networks. In this paper, we consider utilization of Multi-packet Reception (MPR) capability in DTN in order to provide better performance. We introduce our own MPR scheduling scheme, Group Based Scheme (GBS), and compare the performance of DTN with different MPR capabilities. Our works have been evaluated by the simulation using a real vehicular network trace composed of the GPS data of more than 4,000 taxis in Shanghai urban area. The results have demonstrated various impacts of using Multi-Packet Reception (MPR) on performance in DTN. By using MPR in DTN, it outperforms the network without MPR especially when the wireless transmission range increases.
Precision Single-Mode Fiber Spectral Attenuation Measurements

Recent publications on spectral attenuation measurements of single-mode fibers show that the consistency of the results is in the order of 50%. Even larger differences were found in a comparison of measurements performed by 14 laboratories of Western Europe working in the field of optical communications. The details of a measurement apparatus for precision single-mode fiber attenuation measurements are described. Systematic investigations of modifications in the optical configuration have led to an improved stability and repeatability of the measured values. A reproducibility of better than 5% over the spectral range from 950 nm to 1650 nm with a spectral resolution of 5 nm and a dynamic range of 40 dB has been achieved.
Control and Stability Analysis of Limit Cycles in a Hopping Robot

A new stabilizing nonlinear controller for the vertical motion of an electrically actuated hopping robot is introduced and analyzed. The approach starts by finding reference limit cycles from the "passive dynamics" of a mass-spring system. The controller then modulates the system dynamics via the leg actuator during the stance phase to force the system trajectory to converge to this reference limit cycle. The controlled system dynamics is a continuous yet nonsmooth vector field. A piecewise-continuous Lyapunov function and the general forms of Lasalle's invariance and domain of attraction theorems are used to prove global asymptotic stability of the desired limit cycles. It is also shown that the derived passive dynamic cycles are indeed the positive limit sets of the controlled system. The rate of convergence can be adjusted but is limited by actuator constraints.
EFFECTS OF MOISTURE DAMAGE REPAIR ON MICROBIAL EXPOSURE AND HEALTH EFFECTS IN SCHOOLS

ABSTRACT This intervention study was designed to show the effects of the renovation of moisture and mold damaged school building on the schoolchildren’s health and exposure. Microbial sampling from indoor air of the school and a health questionnaire study were performed before and after renovation. The results were compared to those from non-damaged reference school. The effect of a thorough renovation in the damaged school was seen as decreased concentrations of airborne fungi and decreased diversity of mycoflora. There was a significant decrease in the prevalence of the respiratory symptoms among schoolchildren after the renovation. The results show that the symptoms are associated with the moisture damage and that the increased symptom levels can be normalized with proper repair measures. INDEX TERMS Moisture damage, Schools, Renovation, Indoor air microbes, Symptoms, Children INTRODUCTION Respiratory symptoms and other adverse health effects are common among schoolchildren in moisture damaged schools (Haverinen
Adapting Conventional Chinese Word Segmenter for Segmenting Micro-blog Text: Combining Rule-based and Statistic-based Approaches

We describe two adaptation strategies which are used in our word segmentation system in participating the Microblog word segmentation bake-off: Domain invariant information is extracted from the in-domain unlabelled corpus, and is incorporated as supplementary features to conventional word segmenter based on Conditional Random Field (CRF), we call it statistic-based adaptation. Some heuristic rules are further used to post-process the word segmentation result in order to better handle the characters in emoticons, name entities and special punctuation patterns which extensively exist in micro-blog text, and we call it rule-based adaptation. Experimentally, using both adaptation strategies, our system achieved 92.46 points of F-score, compared with 88.73 points of F-score of the unadapted CRF word segmenter on the pre-released development data. Our system achieved 92.51 points of F-score on the final test data.
Dynamic Bayesian Testing of Sets of Variants in Complex Diseases

Rare genetic variants have recently been studied for genome-wide associations with human complex diseases. Existing rare variant methods are based on the hypothesis-testing framework that predefined variant sets need to be tested separately. The power of those methods is contingent upon accurate selection of variants for testing, and frequently, common variants are left out for separate testing. In this article, we present a novel Bayesian method for simultaneous testing of all genome-wide variants across the whole frequency range. The method allows for much more flexible grouping of variants and dynamically combines them for joint testing. The method accounts for correlation among variant sets, such that only direct associations with the disease are reported, whereas indirect associations due to linkage disequilibrium are not. Consequently, the method can obtain much improved power and flexibility and simultaneously pinpoint multiple disease variants with high resolution. Additional covariates of categorical, discrete, and continuous values can also be added. We compared our method with seven existing categories of approaches for rare variant mapping. We demonstrate that our method achieves similar power to the best methods available to date when testing very rare variants in small SNP sets. When moderately rare or common variants are included, or when testing a large collection of variants, however, our method significantly outperforms all existing methods evaluated in this study. We further demonstrate the power and the usage of our method in a whole-genome resequencing study of type 1 diabetes.
Causal Inference in the Age of Decision Medicine

Causal analyses and causal inference is a growing area of biostatics. In parallel, there is increasing focus on using genomic information to guide medical practice, i.e. personalized medicine or decision medicine. This perspective discusses causal inference in the context of personalized or decision medicine, including the assumptions and the concept that the task is different depending on whether the primary goal is the average response of treatment in the population or the ability to characterize the response for an individual or a subgroup. This perspective provides a tutorial of modern causal inference and then provides suggestions how application of specific kinds of causal inference would promote advances in translational sciences. The concept of the subpopulation causal effect is one path toward improved decision medicine. A dataset containing cardiovascular disease risk factor levels and genomic information is analyzed and different causal effects are estimated.