Miracle-dz/UniFAR-FTUs huggingface dataset:
Run-time efficient probabilistic model checking

Unpredictable changes continuously affect software systems and may have a severe impact on their quality of service, potentially jeopardizing the system's ability to meet the desired requirements. Changes may occur in critical components of the system, clients' operational profiles, requirements, or deployment environments. The adoption of software models and model checking techniques at run time may support automatic reasoning about such changes, detect harmful configurations, and potentially enable appropriate (self-)reactions. However, traditional model checking techniques and tools may not be simply applied as they are at run time, since they hardly meet the constraints imposed by on-the-fly analysis, in terms of execution time and memory occupation. This paper precisely addresses this issue and focuses on reliability models, given in terms of Discrete Time Markov Chains, and probabilistic model checking. It develops a mathematical framework for run-time probabilistic model checking that, given a reliability model and a set of requirements, statically generates a set of expressions, which can be efficiently used at run-time to verify system requirements. An experimental comparison of our approach with existing probabilistic model checkers shows its practical applicability in run-time verification.
Generation of anisotropic networks for simulations of signals spreading in excitable cell systems.

We describe a technique of constructing an anisotropic lattice of coupled subunits and apply it to assemblages of excitable myocardial cells conducting an electric impulse. The cells are assumed arranged in a planar network comprising a few thousands of cells, each having at most eight connections to neighbors. The connections are characterized as being one of four kinds: horizontal, vertical, diagonal and inversely diagonal. The connectivity in the cellular network is constructed randomly with prescribed fractions of each of the four kinds of connections. These fractions are taken from literature and characterize certain types of cardiac tissues, the left ventricle in particular. The nodes in the network correspond to cells, which are treated as point objects described by ordinary differential equations. For the model of the left ventricle we study spreading of the action potential initiated by an impulse stimulus of a small
Sampling with Removal in LP-type Problems

Random sampling is an important tool in optimization subject to finitely or infinitely many constraints. Here we are interested in obtaining solutions of low cost that violate only few constraints. Under convexity or similar favorable conditions, and assuming fixed dimension, one can indeed derive combinatorial bounds on the expected number (or probability mass) of constraints violated by the optimal solution subject to a (small) random sample of constraints. The cost of the sample solution, however, cannot be bounded combinatorially. Suppose that for a fixed integer k, we locally minimize the cost by removing k constraints from the sample. Can we still guarantee (good) bounds on the number or mass of violated constraints? This question was asked and partially answered in the framework of chance-constrained optimization [1]. Here we study the question in the completely combinatorial setting of LP-type problems that allows a unified treatment of many different optimization problems. Given a nondegenerate LP-type problem of combinatorial dimension δ, we prove that sampling with removal of the best k constraints induces an LP-type problem of combinatorial dimension O(δk+1), and this bound is tight in the worst case. It follows that through this kind of removal, the expected number of violated constraints blows up by a factor of at most O(δk); for relevant sample sizes, however, a matching lower bound for the blowup factor is not implied. At the same time, our results show that further improvements require new tools that go beyond combinatorial dimension.
Developing Research Agendas on Whole School Improvement Models: The Model Providers' Perspective

The current education policy environment places a heavy emphasis on scientifically based research. This article examines how whole school improvement models approach the development of a research agenda, including what influences and challenges model providers face in implementing their agenda. Responses also detail the advantages and disadvantages model providers see in using internal and external research teams to carry out their research agenda. In addition, the article addresses common themes about how model providers believe their research agenda interacts with the public realm, including how education consumers use research in selecting a whole school improvement model and how their models are portrayed in systematic reviews of their models' research.
Automatic detection of parkinson's disease from words uttered in three different languages

About 90% of the people with Parkinson’s disease (PD) develop speech impairments such as monopitch, monoloudness, imprecise articulation, and other symptoms. There are several studies addressing the problem of the automatic detection of PD from speech signals in order to develop computer aided tools for the assessment and monitoring of the patients. Recent works have shown that it is possible to detect PD from speech with accuracies above 90%; however, it is still unclear whether it is possible to make the detection independent of the spoken language. This paper addresses the automatic detection of PD considering speech recordings of three languages: German, Spanish and Czech. According to the results it is possible to classify between speech of people with PD and healthy controls (HC) with accuracies ranging from 84% to 99%, depending on the utterance. Index Terms: Parkinson’s disease, dysarthria, speech disorder, language, acoustic measures, voiced/unvoiced segments.
Audio-Visual Feature Fusion for Vehicles Classification in a Surveillance System

In this paper we tackle the challenging problem of multimodal feature selection and fusion for vehicle categorization. Our proposed framework utilizes a boosting-based feature learning technique to learn the optimal combinations of feature modalities. New multimodal features are learned from the existing uni-modal features which are initially extracted from the data acquired by a novel audio-visual sensing system under different sensing conditions (long range, moving vehicles, and various environments). Experiments on a challenging dataset collected with our long-range sensing system demonstrated that the proposed technique is robust to noise and can find the best among multiple good feature modalities from training in terms of classification performance than the feature modality selection using a sequential based technique which tends to stay on a local maxima.
Evolutionary coalitional games: design and challenges in wireless networks

The current state of the art of evolutionary coalitional game theory in wireless networking and communications is captured in this article, where solutions are discussed under their related applications. Fully distributed algorithms for global optima in network selection games are examined. Then the problem of dynamic network formation and evolutionary coalitional games in wireless networks are investigated. This article also points out the open research issues, new interests, and developments in this interdisciplinary field.
Limited contemporary gene flow and high self-replenishment drives peripheral isolation in an endemic coral reef fish

Extensive ongoing degradation of coral reef habitats worldwide has lead to declines in abundance of coral reef fishes and local extinction of some species. Those most vulnerable are ecological specialists and endemic species. Determining connectivity between locations is vital to understanding recovery and long-term persistence of these species following local extinction. This study explored population connectivity in the ecologically-specialized endemic three-striped butterflyfish (Chaetodon tricinctus) using mt and msatDNA (nuclear microsatellites) to distinguish evolutionary versus contemporary gene flow, estimate self-replenishment and measure genetic diversity among locations at the remote Australian offshore coral reefs of Middleton Reef (MR), Elizabeth Reef (ER), Lord Howe Island (LHI), and Norfolk Island (NI). Mt and msatDNA suggested genetic differentiation of the most peripheral location (NI) from the remaining three locations (MR, ER, LHI). Despite high levels of mtDNA gene flow, there is limited msatDNA gene flow with evidence of high levels of self-replenishment (≥76%) at all four locations. Taken together, this suggests prolonged population recovery times following population declines. The peripheral population (NI) is most vulnerable to local extinction due to its relative isolation, extreme levels of self-replenishment (95%), and low contemporary abundance.
Weighted Multidimensional Search and Its Application to Convex Optimization

We present a weighted version of Megiddo's multidimensional search technique and use it to obtain faster algorithms for certain convex optimization problems in $\Reals^d$, for fixed $d$. This leads to speed-ups by a factor of $\log^d n$ for applications such as solving the Lagrangian duals of matroidal knapsack problems and of constrained optimum subgraph problems on graphs of bounded tree-width.
Genome Sequence of a Marine Spirillum, Oceanospirillum multiglobuliferum ATCC 33336T, Isolated from Japan

ABSTRACT Oceanospirillum multiglobuliferum ATCC 33336T is a motile gammaproteobacterium with bipolar tufted flagella, noted for its low salt tolerance compared to other marine spirilla. This strain was originally isolated from the putrid infusions of Crassostrea gigas near Hiroshima, Japan. This paper presents a draft genome sequence for O. multiglobuliferum ATCC 33336T.
The Formation and Evolution of Planetary Systems: First Results from a Spitzer Legacy Science Program

We present 3-160 μm photometry obtained with the Infrared Array Camera (IRAC) and Multiband Imaging Photometer for Spitzer (MIPS) instruments for the first five targets from the Spitzer Space Telescope Legacy Science Program "Formation and Evolution of Planetary Systems" and 4-35 μm spectrophotometry obtained with the Infrared Spectrograph (IRS) for two sources. We discuss in detail our observations of the debris disks surrounding HD 105 (G0 V, 30 ± 10 Myr) and HD 150706 (G3 V, ∼700 ± 300 Myr). For HD 105, possible interpretations include large bodies clearing the dust inside of 45 AU or a reservoir of gas capable of sculpting the dust distribution. The disk surrounding HD 150706 also exhibits evidence of a large inner hole in its dust distribution. Of the four survey targets without previously detected IR excess, spanning ages 30 Myr to 3 Gyr, the new detection of excess in just one system of intermediate age suggests a variety of initial conditions or divergent evolutionary paths for debris disk systems orbiting solar-type stars.
Video processing and 3D modelling of chest movement using MS Kinect depth sensor

General methods of video processing and three dimensional modelling have a wide range of applications in engineering, archaeology and spacial objects study. The paper is devoted to applications of these methods in biomedicine and neurology using MS Kinect depth sensor for non-contact monitoring of breathing. A special attention is paid to visualization of results and motion mapping over the selected chest area. The proposed methodology applies digital signal processing methods and functional transforms for acquired data de-noising, spectral analysis, and feature selection. Suggested method uses further the local polynomial approximation to detect extremal values of spectral components. The results verify the correspondence between the evaluations of the breathing frequency obtained from the thorax movement recorded by the depth sensor. The study proves that simple depth sensors can be used for non-contact detection of breathing frequency and for the three dimensional modelling of the chest movement. The proposed non-contact method enables to analyse breathing for diagnostic purposes and monitoring in the home environment as a component of assisted living technologies. General methodology studied form a contribution to the use of video sequences or sets of images for spacial objects modelling, their recognition, possible three dimensional printing or analysis of time evolution of their features.
SWS: A Smart Walk mechanism for resources search in unstructured mobile P2P networks

In this paper, we propose SWS: a Smart Walk mechanism for resources Search in unstructured mobile P2P networks. SWS is a smart version of the conventional Random Walk used for resource search in unstructured P2P networks. The advantage of SWS is the awareness of both the overlay and mobile underlay constraints during the resources search process. The search query is forwarded from peer to peer following three smart conditions concerning each candidate neighbor which are: its physical distance from the current peer, its remaining energy and its ability (which will be defined later). Simulation results show that our mechanism offers good performances comparing to other existing search mechanisms.
On the Selection of Transmission Range in Underwater Acoustic Sensor Networks

Transmission range plays an important role in the deployment of a practical underwater acoustic sensor network (UWSN), where sensor nodes equipping with only basic functions are deployed at random locations with no particular geometrical arrangements. The selection of the transmission range directly influences the energy efficiency and the network connectivity of such a random network. In this paper, we seek analytical modeling to investigate the tradeoff between the energy efficiency and the network connectivity through the selection of the transmission range. Our formulation offers a design guideline for energy-efficient packet transmission operation given a certain network connectivity requirement.
Intentional Objects, Pretence, and the Quasi-Relational Nature of Mental Phenomena: A New Look at Brentano on Intentionality

Abstract Brentano famously changed his mind about intentionality between the 1874 and 1911 editions of Psychology from an Empirical Standpoint (PES). The 1911 edition repudiates the 1874 view that to think about something is to stand in a relation to something that is within in the mind, and holds instead that intentionality is only like a relation (it is ‘quasi-relational’). Despite this, Brentano still insists that mental activity involves ‘the reference to something as an object’, much as he did in the 1874 edition of PES. The question is what Brentano might have meant by this, given that he rejects a relational account of intentionality. The present paper suggests an answer. It draws on recent work on pretence theory to provide a model of Brentano’s notion of the quasi-relational nature of mental phenomena, as well as of the notion of mental reference to an object, and argues that the model helps to explain why Brentano might have been able discern a clear continuity between the views of the 1874 and 1911 editions of PES, despite the differences.
Worms need microbes too: microbiota, health and aging in Caenorhabditis elegans

Many animal species live in close association with commensal and symbiotic microbes (microbiota). Recent studies have revealed that the status of gastrointestinal tract microbiota can influence nutrition‐related syndromes such as obesity and type‐2 diabetes, and perhaps aging. These morbidities have a profound impact in terms of individual suffering, and are an increasing economic burden to modern societies. Several theories have been proposed for the influence of microbiota on host metabolism, but these largely remain to be proven. In this article we discuss how microbiota may be manipulated (via pharmacology, diet, or gene manipulation) in order to alter metabolism, immunity, health and aging in the host. The nematode Caenorhabditis elegans in combination with one microbial species is an excellent, defined model system to investigate the mechanisms of host–microbiota interactions, particularly given the combined power of worm and microbial genetics. We also discuss the multifaceted nature of the worm–microbe relationship, which likely encompasses predation, commensalism, pathogenicity and necromeny.
Research Advances of Microencapsulation and Its Prospects in the Petroleum Industry

Additives in the petroleum industry have helped form an efficient system in the past few decades. Nowadays, the development of oil and gas has been facing more adverse conditions, and smart response microcapsules with the abilities of self-healing, and delayed and targeted release are introduced to eliminate obstacles for further exploration in the petroleum industry. However, limited information is available, only that of field measurement data, and not mechanism theory and structural innovation data. Thus we propose that the basic type, preparation, as well as mechanism of microcapsules partly depend on other mature fields. In this review, we explore the latest advancements in evaluating microcapsules, such as X-ray computed tomography (XCT), simulation, and modeling. Finally, some novel microencapsulated additives with unparalleled advantages, such as flexibility, efficiency, and energy-conservation are described.
THE BIOLOGY OF SPECIATION

Since Darwin published the “Origin,” great progress has been made in our understanding of speciation mechanisms. The early investigations by Mayr and Dobzhansky linked Darwin's view of speciation by adaptive divergence to the evolution of reproductive isolation, and thus provided a framework for studying the origin of species. However, major controversies and questions remain, including: When is speciation nonecological? Under what conditions does geographic isolation constitute a reproductive isolating barrier? and How do we estimate the “importance” of different isolating barriers? Here, we address these questions, providing historical background and offering some new perspectives. A topic of great recent interest is the role of ecology in speciation. “Ecological speciation” is defined as the case in which divergent selection leads to reproductive isolation, with speciation under uniform selection, polyploid speciation, and speciation by genetic drift defined as “nonecological. ” We review these proposed cases of nonecological speciation and conclude that speciation by uniform selection and polyploidy normally involve ecological processes. Furthermore, because selection can impart reproductive isolation both directly through traits under selection and indirectly through pleiotropy and linkage, it is much more effective in producing isolation than genetic drift. We thus argue that natural selection is a ubiquitous part of speciation, and given the many ways in which stochastic and deterministic factors may interact during divergence, we question whether the ecological speciation concept is useful. We also suggest that geographic isolation caused by adaptation to different habitats plays a major, and largely neglected, role in speciation. We thus provide a framework for incorporating geographic isolation into the biological species concept (BSC) by separating ecological from historical processes that govern species distributions, allowing for an estimate of geographic isolation based upon genetic differences between taxa. Finally, we suggest that the individual and relative contributions of all potential barriers be estimated for species pairs that have recently achieved species status under the criteria of the BSC. Only in this way will it be possible to distinguish those barriers that have actually contributed to speciation from those that have accumulated after speciation is complete. We conclude that ecological adaptation is the major driver of reproductive isolation, and that the term “biology of speciation,” as proposed by Mayr, remains an accurate and useful characterization of the diversity of speciation mechanisms.
Learning Coefficients of Layered Models When the True Distribution Mismatches the Singularities

Hierarchical learning machines such as layered neural networks have singularities in their parameter spaces. At singularities, the Fisher information matrix becomes degenerate, with the result that the conventional learning theory of regular statistical models does not hold. Recently, it was proved that if the parameter of the true distribution is contained in the singularities of the learning machine, the generalization error in Bayes estimation is asymptotically equal to/n, where 2 is smaller than the dimension of the parameter andn is the number of training samples. However, the constant strongly depends on the local geometrical structure of singularities; hence, the generalization error is not yet clarified when the true distribution is almost but not completely contained in the singularities. In this article, in order to analyze such cases, we study the Bayes generalization error under the condition that the Kullback distance of the true distribution from the distribution represented by singularities is in proportion to 1/n and show two results. First, if the dimension of the parameter from inputs to hidden units is not larger than three, then there exists a region of true parameters such that the generalization error is larger than that of the corresponding regular model. Second, if the dimension from inputs to hidden units is larger than three, then for arbitrary true distribution, the generalization error is smaller than that of the corresponding regular model.
Comparison of single‐particle analysis and electron tomography approaches: an overview

Three‐dimensional structure of a wide range of biological specimens can be computed from images collected by transmission electron microscopy. This information integrated with structural data obtained with other techniques (e.g., X‐ray crystallography) helps structural biologists to understand the function of macromolecular complexes and organelles within cells. In this paper, we compare two three‐dimensional transmission electron microscopy techniques that are becoming more and more related (at the image acquisition level as well as the image processing one): electron tomography and single‐particle analysis. The first one is currently used to elucidate the three‐dimensional structure of cellular components or smaller entire cells, whereas the second one has been traditionally applied to structural studies of macromolecules and macromolecular complexes. Also, we discuss possibilities for their integration with other structural biology techniques for an integrative study of living matter from proteins to whole cells.
Support vector classification with fuzzy hyperplane

In this paper, we develop a novel support vector algorithm with fuzzy hyperplane for pattern classification. We first introduce the concepts of fuzzy hyperplane and fuzzy linear separability. Then, the proposed approach seeks a fuzzy hyperplane that best separates the positive class from the negative class with the widest margin in the feature space. Further, the decision function of the proposed approach is generalized so the values assigned to the individuals fall within a specified range and indicate the membership degree of these individuals in a given category. This integration preserves the benefits of fuzzy set theory and SVM theory, where the use of the fuzzy hyperplane provides the SVM with effective means for capturing the approximate, imprecise nature of the real world. On the other hand, the SVM provides the advantage to minimize the structural risk and effectively generalize the unseen data. Experimental results are then presented which indicate the performance of the proposed approach.