Miracle-dz/UniFAR-FTUs huggingface dataset:
Calonectria diseases on ornamental plants in Europe and the Mediterranean basin: an overview.

Species of Calonectria and their cylindrocladium-like asexual morphs are important plant pathogens of agronomic and forestry crops, especially in the tropical and subtropical regions of the world. Calonectria species have been associated with a wide range of disease symptoms on a large number of plant hosts. On horticultural crops, most records of Calonectria species come from the Northern Hemisphere, where they occur mainly in gardens and ornamental nurseries. In Europe and the Mediterranean basin, several species are widespread in nurseries and cause extensive damage to ornamental plants. In the past, identification of species was based on phenotypic characters and sexual compatibility using standardised media. More recently, morphological characteristics, phylogenetic studies (DNA sequence data of the β-tubulin, histone H3 and translation elongation factor-1α gene regions) and mating studies have revealed the presence of several cryptic species complexes that were formerly treated as single Calonectria species. These studies resulted in the introduction of several new species. Other studies aimed at understanding environmental sustainability focused attention on soil solarisation and biological control as means for controlling these pathogens. The potential use of biological control agents (BCAs) and chemicals for controlling Calonectria-induced diseases has recently been addressed. In this review we discuss the Calonectria species detected in Europe and the Mediterranean basin, and the disease management strategies. In view of the mandatory implementation of integrated pest management (IPM) for all European countries by 2014, this paper provides basic information as a platform for the adaptation of more sustainable integrated measures to control Calonectria diseases in European nurseries.
Emulating quantum computation with artificial neural networks.

We demonstrate, that artificial neural networks (ANN) can be trained to emulate single or multiple basic quantum operations. In order to realize a quantum state, we implement a novel "quantumness gate" that maps an arbitrary matrix to the real representation of a positive hermitean normalized density matrix. We train the CNOT gate, the Hadamard gate and a rotation in Hilbert space as basic building blocks for processing the quantum density matrices of two entangled qubits. During the training process the neural networks learn to represent the complex structure, the hermiticity, the normalization and the positivity of the output matrix. The requirement of successful training allows us to find a critical bottleneck dimension which reflects the relevant quantum information. Chains of individually trained neural quantum gates can be constructed to realize any unitary transformation. For scaling to larger quantum systems, we propose to use correlations of stochastic macroscopic two-level observables or classical bits. This novel concept provides a path for a classical implementation of computationally relevant quantum information processing on classical neural networks, in particular on neuromorphic computing machines featuring stochastic operations.
Bibliographic Instruction and Postmodern Pedagogy.

Postmodernist debates in academic circles provide expanded opportunities for making information studies an integrated part of the academic program. Past bibliographic instruction (BI) practices have been based on a reductionist scientific model that dislocates the focus of instruction from the documents of a discipline to a structure of disciplinary literature frequently imposed from without and often having little to do with the information content. Postmodern concepts provided by textual criticism and the sociology of knowledge can turn libraries into learning laboratories for studying information in the context of the academic discourses that create it. Postmodernist approaches allow students to compare, at one level, methods of information organization and, at a more basic level, how knowledge is claimed in a variety of scholarly disciplines. Rhetoricians, especially composition teachers with whom bibliographic instruction librarians have much in common, have already realized the importance of postmodernism as a strategy for teaching composition. Rhetorician Richard Lanham even maintains that elements of postmodernism applied to emerging information technology provide ways to reform the undergraduate curriculum.
Unsupervised Learning of Object Landmarks through Conditional Image Generation

We propose a method for learning landmark detectors for visual objects (such as the eyes and the nose in a face) without any manual supervision. We cast this as the problem of generating images that combine the appearance of the object as seen in a first example image with the geometry of the object as seen in a second example image, where the two examples differ by a viewpoint change and/or an object deformation. In order to factorize appearance and geometry, we introduce a tight bottleneck in the geometry-extraction process that selects and distils geometry-related features. Compared to standard image generation problems, which often use generative adversarial networks, our generation task is conditioned on both appearance and geometry and thus is significantly less ambiguous, to the point that adopting a simple perceptual loss formulation is sufficient. We demonstrate that our approach can learn object landmarks from synthetic image deformations or videos, all without manual supervision, while outperforming state-of-the-art unsupervised landmark detectors. We further show that our method is applicable to a large variety of datasets - faces, people, 3D objects, and digits - without any modifications.
Using Deep Learning to Evaluate the Segmentation of Liver Cell from Biopsy Image

Liver cancer is one of the most critical health problems in the world. The grading diagnosis for liver cancer in biopsy images is essential for the treatment of liver cancer and disease prognosis. A grading system that uses artificial intelligence to provide quantitative and objective results for physicians and pathologists; it will not only save time but also improve the accuracy of diagnosis. In the grading system, the main work is grading with the nucleus segmented from cancer biopsy images. However, improper focus and complex stroma background will affect the performance of segmentation. If we can evaluate segmentation of the nucleus and exclude the failed segmentation from the grading system, it will significantly improve the accuracy of the grading. In this paper, we propose a method with deep learning for evaluating the segmentation of liver nucleus, and the experimental results demonstrate that the performance of our method is 90.5%.
The effect of geographical distance on community detection in flow networks

A flow network is a directed graph in which each edge has an origin and a destination, and the edge represents movement from one point to another. With constantly increasing number of devices and methods for collecting location information, flow networks are becoming larger, denser and more complex. Many physical models have been developed for exploring the structure of flow networks, however in most cases interpretation considers only the number of flows and ignores relevant geographical context. In this paper we demonstrate how geographic distance can be used to improve the process of a standard physical model for flow network structure: community detection. We incorporate distance between two areas in the computation of the Louvain community detection algorithm. The results are compared to the result of the original method using a flow data set on commuting flows in Scotland. A preliminary evaluation of results shows that including geographical distance enables us to obtain a different insight from the network and provides more detailed information about local patterns of commuting in Scotland, which is lost in a non-spatial version of community detection.
Genetic Structure of Wild European Sea Bass ( Dicentrarchus labrax L, 1758) Populations in Aegean and Levantine Sea Using Microsatellite Markers

The aim of this study is to investigate the genetic structure of Dicentrarchus labrax populations sampled in the NorthEast Mediterranean. These are the main areas where the hatcheries collect their broodstock candidates from the wild in Turkey, which is the biggest European sea bass producer in Europe. Five samples collected from the Turkish Levantine and Aegean Sea coasts were analysed in addition to the Atlantic and Ionian samples (total 305 individuals) for 12 microsatellite loci. The present results revealed that the Aegean populations from Homa and Doğanbey, where the sea bass culture is mostly conducted in Turkish Aegean Sea, were closely related (FST 0.00347, P>0.01). Another close relation was found between Yumurtalık and Doğanbey (FST 0.01148, P>0.01), which might be the result of massive fry transfers from Yumurtalık (East Levantine coast) to Doğanbey till 2000 in Turkey. Obtained results also show gene flow from Greek to Turkish Aegean population which most probably was the consequence of frequent juvenile transfers from Greek hatcheries to Turkish fish farms between 2000 and 2010.
1 EVALUATING SUBDIVISION CHARACTERISTICS ON SINGLE FAMILY HOUSING VALUE USING HIERARCHICAL LINEAR MODELLING Contact

This research quantifies the financial effects of value creation concepts in relation to housing values in a single-family residential development using hierarchical linear modeling (HLM). As a result, value creation concepts such as sense of arrival, greenway connectivity, and the median length of cul-de-sac have positive effects on single family housing values while the number of accessible entrances and the median length of block variables have negative effects on single family housing values. These results indicate that higher values in a subdivision may result from smaller blocks, interconnected greenways, and a single entrance which provides a sense of arrival.
A comparative study of the removal of lead, cadmium and zinc ions from aqueous solutions by natural and Fe(III)-modified zeolite

The main purpose of this research was to explain the differences in the adsorption mechanisms and adsorption capacities of natural and Fe(III)-modified zeolite for Pb2+, Zn2+ and Cd2+ ions. The adsorbents were characterized with respect to their phase composition, morphology, specific surface area, cation exchange capacity and point of zero charge. Batch adsorption experiments were performed as a function of the initial ion concentration and temperature, at constant initial pH value of 5.5 ± 0.1. The adsorption isotherms at 25, 40, 55 and 70°C suggest that the sequence of adsorption efficiency of both zeolites is Pb2+ > Zn2+ > Cd2+. The adsorption capacities of both zeolites increased with increasing temperature. The equilibrium adsorption data were best described by the Langmuir adsorption isotherm. This study revealed that the adsorption capacity of the Fe(III)-modified zeolite is much higher than that of natural zeolite for all investigated ions owing to the higher: specific adsorption caused by the new functional groups formed on the zeolite surface; ion exchange due to the presence of easily exchangeable ions; and hydroxide precipitation caused by higher point of zero charge of the Fe(III)-modified zeolite compared to natural zeolite. [Projekat Ministarstva nauke Republike Srbije, br. III 45019]
Structure characterization and precipitation in two Al-Mg-Si-Mn casting alloys

The as-cast and heat treated structure of permanent mould and high pressure die castings of the AlMg5Si2Mn alloy has been investigated by differential scanning calorimetry, microhardness measurements, transmission electron microscopy and energy dispersive X-ray analysis. Inside the α-Al grains curved plate-like precipitates were detected for both alloys. Examination of these precipitates revealed a number of features, such as: (i) the composition of the precipitates is very close to the stochiometric Mg 2 Si compound; (ii) precipitates are aligned along dislocations; (iii) the precipitate density is much higher for the high pressure die castings where the α-Al matrix contains more dislocations than in permanent mould castings; (vi) precipitates lie inside the α-Al grains where they are randomly distributed. Between the Mg 2 Si lamellas precipitates were not observed; (v) homogenization of the alloy results in complete dissolution of the precipitates and during artificial aging new needle-shaped precipitates are formed.
The effects of the frequency of spatially proximate and distant interaction on innovation by Irish SMEs

This paper tests whether more frequent interaction at different spatial levels has a positive effect on the innovation performance of small- and medium-sized enterprises (SMEs) in the South-West and South-East of Ireland. Based on an original survey, it finds that more frequent interaction generally increases innovation likelihood, but at a diminishing rate, thus suggesting a trade-off between resources dedicated to transforming knowledge into new products and processes. Spatially distant interaction is found to be at least as valuable as proximate interaction, which questions the received wisdom that the best sources of knowledge are regional. Given the value of distant interaction, the results indicate that regional lock-in may be an obstacle to superior innovation performance of SMEs.
Three Facets of Roles in Foundational Ontologies

Roles remain such a nebulous concept notwithstanding its ubiquity in a wide variety of domains, including conceptual modeling, that no clear consensus exists over the nature of roles in the foundational ontology research. In this paper I argue that there are three closely intertwined, but conceptually separate role-related notions: a role specification, a role position, and a role performance. I further contend that different accounts of roles might depend on which of the three concepts takes priority over the other two. Additionally I propose that there be three possible interpretations of the ontological nature of roles, each of which requires careful cost-benefit analysis: a family resemblance concept, a functionally definable concept, and a practically unifiable concept.
The impact of spatial fluctuations in the ultraviolet background on intergalactic carbon and silicon

Spatial inhomogeneities in the spectral shape of the ultraviolet background (UVB) at the tail-end of He ii reionization are thought to be the primary cause of the large fluctuations observed in the He ii to H i Lyα forest optical depth ratio, , at z≃ 2–3. These spectral hardness fluctuations will also influence the ionization balance of intergalactic metals; we extract realistic quasar absorption spectra from a large hydrodynamical simulation to examine their impact on intergalactic Si iv and C iv absorbers. Using a variety of toy UVB models, we find that while the predicted spatial inhomogeneities in spectral hardness have a significant impact on , the longer mean free path for photons with frequencies above and below the He ii ionization edge means these fluctuations have less effect on the Si iv and C iv ionization balance. Furthermore, UVB models which produce the largest fluctuations in specific intensity at the He ii ionization edge also have the softest ionizing spectra, and thus result in photoionization rates which are too low to produce significant fluctuations in the observed . Instead, we find spatial variations in the IGM metallicity will dominate any scatter in . Our results suggest that observational evidence for homogeneity in the observed distribution does not rule out the possibility of significant fluctuations in the UVB spectral shape at z≃ 2–3. On the other hand, the scatter in metallicity inferred from observations of intergalactic C iv and Si iv absorption at z≃ 2–3 using spatially uniform ionization corrections is likely intrinsic, and therefore provides a valuable constraint on intergalactic metal enrichment scenarios at these redshifts.
Inflationary correlation functions without infrared divergences

Inflationary correlation functions are potentially affected by infrared divergences. For example, the two-point correlator of curvature perturbation at momentum k receives corrections ~ ln (kL), where L is the size of the region in which the measurement is performed. We define infrared-safe correlation functions which have no sensitivity to the size L of the box used for the observation. The conventional correlators with their familiar log-enhanced corrections (both from scalar and tensor long-wavelength modes) are easily recovered from our IR-safe correlation functions. Among other examples, we illustrate this by calculating the corrections to the non-Gaussianity parameter fNL coming from long-wavelength tensor modes. In our approach, the IR corrections automatically emerge in a resummed, all-orders form. For the scalar corrections, the resulting all-orders expression can be evaluated explicitly.
Multi-label kNN Classifier with Self Adjusting Memory for Drifting Data Streams

Multi-label data streams is a highly challenging task involving drifts in features and labels. Classifiers must automatically adapt to changes while keeping a competitive accuracy in a real-time dynamic environment where the frequencies of the labelsets are non-stationary and highly imbalanced. This paper presents a multi-label k Nearest Neighbor (kNN) with Self Adjusting Memory (SAM) for drifting data streams (ML-SAM-kNN). It exploits shortand long-term memories to predict the current and evolving states of the data stream. The experimental study compares the proposal with eight other multi-label classifiers for data streams on 23 datasets on six multi-label metrics, evaluation time, and memory consumption. Non-parametric statistical analysis of the results shows the superiority of ML-SAMkNN, including when compared with ML-kNN.
Semi-Supervised Learning with Explicit Misclassification Modeling

To harness modern multi-core processors, it is imperative to develop parallel versions of fundamental algorithms. In this paper, we present a general approach to best-first heuristic search in a shared-memory setting. Each thread attempts to expand the most promising open nodes. By using abstraction to partition the state space, we detect duplicate states without requiring frequent locking. We allow speculative expansions when necessary to keep threads busy. We identify and fix potential livelock conditions in our approach, verifying its correctness using temporal logic. In an empirical comparison on STRIPS planning, grid pathfinding, and sliding tile puzzle problems using an 8-core machine, we show that A* implemented in our framework yields faster search than improved versions of previous parallel search proposals. Our approach extends easily to other best-first searches, such as Anytime weighted A*.
Detecting and Classifying Cognitive Activity Based on Changes in Cerebral Blood Flow Velocity

Detecting and Classifying Cognitive Activity Based on Changes in Cerebral Blood Flow Velocity Andrew James Byron Myrden Master of Applied Science Graduate Department of Institute of Biomaterials and Biomedical Engineering University of Toronto 2011 Individuals with severe physical impairments have a reduced ability to communicate through movement and speech. We investigated transcranial Doppler ultrasound as a potential measurement modality for a novel brain-computer interface. It was hypothesized that cognitive activity would result in detectable changes in cerebral blood flow velocity within the middle cerebral arteries. Nine able-bodied participants alternated between rest and two different mental activities silent word generation and mental rotation. Two analyses were performed to assess the feasibility and practicality of a TCD-based brain-computer interface. Both mental activities were independently differentiated from rest with high accuracy. Intuitive time-domain features were sufficient for classification. Data transmission rate was quadrupled by differentiating all three classes simultaneously using shorter state durations. Transcranial Doppler ultrasound can be used to automatically detect cognitive activity and may be useful as the basis of a brain-computer interface.
NeuroCYCLOPS: A Novel System for Endoscopic Neurosurgery

In many minimally invasive surgical (MIS) procedures including intraparenchymal brain tumour resection, endoscopic laryngeal microsurgery, transanal endoscopic microsurgery (TEM), endoscopic lumbar discectomy and tubular retraction devices are used to create and maintain a narrow corridor to the area of interest while causing minimal harm to surrounding tissue. In these procedures tissue manipulation is achieved by inserting long rigid instruments into the corridor. However, the geometry of the retraction device, in combination with the length of the instruments, limits the dexterity and control of the instrument. In previous studies, the CYCLOPS system has shown to be able to overcome these limitations by offering 5 or 6 DOF while maintaining force feedback capabilities in general surgical procedures [1]. The present study applies the same tendon-driven parallel manipulation principle of the CYCLOPS to microsurgical procedures that use tubular retraction devices. Among the most common microsurgical procedures performed within the brain is the microsurgical resection of intrinsic brain tumours. The 11.5mm diameter Neuroendoport (NEP) (UPMC, Pittsburgh, PA) has been used for nearly atraumatic brain tissue retraction to gain transparenchymal access to deep-seated lesions [2]. However, approaches using the NEP are very technically challenging and have not been widely adopted by the neurosurgical community [3]. Here, we report the development and early validation of the neuroCYCLOPS, a manually controlled parallel-driven manipulator for NEP surgery.
Do Inflation-Targeting Central Banks Implicitly Target the Price Level?

This paper reports graphical and statistical evidence that the inflation targeting regimes in Canada and the UK - but not in Australia, New Zealand, or Sweden - actually resemble price-level targeting. In particular, the price level closely tracks the path implied by the inflation target, and the time-series predictions of the "bygones-are-bygones" version of inflation targeting are rejected by the data in favor of those implied by price-level targeting. These results indicate heterogeneity in the actual application of inflation targeting across countries and, for Canada and the UK, imply that the characterization of inflation targeting as a policy where shocks are accommodated is at odds with the data. Moreover, up to extent that their current policies already resemble price-level targeting, the welfare gains of replacing inflation with (explicit) price-level targeting are likely to be small.
Effect of an immune challenge on the anti-predator response of the green Iberian frog (Pelophylax perezi): The influence of urban habitats

This work was supported by Donostia city council and the Spanish Ministry of Education and Culture with a pre-doctoral grant to M.I-C (grant number FPU12/04148). All capture and handling of frogs complied with existing laws regulating the treatment of animals in Spain. The study was performed under a capture and handing licence from the Gipuzkoa Administration (number 1695). M.I. C. and C.C. conducted this work with Spanish accreditation to conduct experiments with animals R.D 53/2013 (ref. number 10/096442.9/13).