Miracle-dz/UniFAR-FTUs huggingface dataset:
High particulate iron(II) content in glacially sourced dusts enhances productivity of a model diatom

Diatoms can access mineral iron from dust if it is reduced, and reduced particulate iron is common in glacial dust sources. Little is known about the bioavailability of iron (Fe) in natural dusts and the impact of dust mineralogy on Fe utilization by photosynthetic organisms. Variation in the supply of bioavailable Fe to the ocean has the potential to influence the global carbon cycle by modulating primary production in the Southern Ocean. Much of the dust deposited across the Southern Ocean is sourced from South America, particularly Patagonia, where the waxing and waning of past and present glaciers generate fresh glaciogenic material that contrasts with aged and chemically weathered nonglaciogenic sediments. We show that these two potential sources of modern-day dust are mineralogically distinct, where glaciogenic dust sources contain mostly Fe(II)-rich primary silicate minerals, and nearby nonglaciogenic dust sources contain mostly Fe(III)-rich oxyhydroxide and Fe(III) silicate weathering products. In laboratory culture experiments, Phaeodactylum tricornutum, a well-studied coastal model diatom, grows more rapidly, and with higher photosynthetic efficiency, with input of glaciogenic particulates compared to that of nonglaciogenic particulates due to these differences in Fe mineralogy. Monod nutrient accessibility models fit to our data suggest that particulate Fe(II) content, rather than abiotic solubility, controls the Fe bioavailability in our Fe fertilization experiments. Thus, it is possible for this diatom to access particulate Fe in dusts by another mechanism besides uptake of unchelated Fe (Fe′) dissolved from particles into the bulk solution. If this capability is widespread in the Southern Ocean, then dusts deposited to the Southern Ocean in cold glacial periods are likely more bioavailable than those deposited in warm interglacial periods.
Path Materialization Revisited: An Efficient Storage Model for XML Data

XML is emerging as a new major standard for representing data on the world wide web. Several XML storage models have been proposed to store XML data in different database management systems. The unique feature of model-mapping-based approaches is that no DTD information is required for XML data storage. In this paper, we present a new model-mapping-based storage model, called XParent. Unlike the existing work on model-mapping-based approaches that emphasized on converting XML documents to/from database schema and translation of XML queries into SQL queries, in this paper, we focus ourselves on the effectiveness of storage models in terms of query processing. We study the key issues that affect query performance, namely, storage schema design (storing XML data across multiple tables) and path materialization (storing path information in databases). We show that similar but different storage models significantly affect query performance. A performance study is conducted using three data sets and query sets. The experimental results are presented.
Interactive Antarctica: a museum installation based on an augmented reality system

This paper considers the research work behind an interactive installation based on an Augmented Reality System (ARS). The installation will be a museum display, and will enable the participant to gesturally interact with data (digital image and sound) from the Antarctic. The ARS will allow a technological exploration of documentary and will considerably develop the contemporary status and understanding of the documentary genre in installation art. In this paper, we argue that the augmented reality installation presents a new mode of documentary experience, thus transforming the genre into a vastly new and complex ensemble. The fact that genre has always been fluid and mutable is widely understood; this paper extends that knowledge by showing the transformative possibilities when an ARS is adopted as the presentation vehicle.
Conditional high copy number ColE1 mutants: resistance to RNA1 inhibition in vivo and in vitro.

We describe three independently isolated copy number mutants of a plasmid ColE1 derivative which undergo temperature‐ and growth‐phase‐dependent DNA amplification in Escherichia coli. These mutants have single base‐pair alterations in a highly localized region of the plasmid genome encoding the replication primer RNA. The mutations map immediately upstream of the RNA1 transcript, altering the sequence between conserved elements of the RNA1 promoter. These mutants have 2‐ to 4‐fold increased copy number relative to wild‐type plasmids in exponential growth at 37 degrees C but undergo 20‐fold amplification of copy number relative to wild‐type when cells enter stationary phase. Cells containing these plasmids grow with normal kinetics at 37 degrees C but grow poorly at 42 degrees C. The poor growth is associated with high‐level plasmid amplification. Both the temperature and growth phase plasmid DNA amplification are suppressed if the ColE1 rop gene product is provided in trans from a compatible plasmid. Analysis of steady‐state RNA1 levels indicates that DNA amplification occurs in the presence of RNA1 made by the mutant plasmid. Thus, the DNA amplification of the copy mutant is not due to an inability to synthesize RNA1. Using an in vitro transcription system containing RNase H, we show that mutant primer processing by RNase H is resistant to levels of the replication initiation inhibitor RNA1 that inhibit wild‐type primer processing. The defect in inhibition appears not to be at the level of association of RNA1 with nascent primer. These results indicate that mutant plasmid amplification is due to the ability of its primer precursor transcripts to serve as substrates for RNase H despite the presence of RNA1.
H-Convergence and Numerical Schemes for Elliptic Problems

We study the convergence of two coupled numerical schemes, which are a discretization of a so-called elliptic-hyperbolic system. Only weak convergence properties are proved on the discrete diffusion of the elliptic problem, and an adaptation of the H-convergence method gives a convergence property of the elliptic part of the scheme. The limit of the approximate solution is then the solution of an elliptic problem, the diffusion of which is not in the general case the H-limit of the discrete diffusion. In a particular case, a kind of weak limit is then obtained for the hyperbolic equation.
Learning Object Affordances for Tool Use and Problem Solving in Cognitive Robots

One of the hallmarks of human intelligence is the ability of predicting the consequences of actions and efficiently plan behaviors based on such predictions. This ability is supported by internal models that human babies acquire incrementally during development through sensorimotor experience: i.e. by interacting with objects in the environment while being exposed to sensori perception. An elegant and powerful concept to represent these internal models has been proposed in developmental psychology under the name of object affordances: action possibilities that an object offers to an agent. Affordances are learned ecologically by the agent and exploited for action planning. Clearly, endowing artificial agents with such cognitive capabilities is a fundamental challenge both in artificial intelligence and robotics. We propose a learning framework in which an embodied agent (i.e. in our case, the humanoid robot iCub) autonomously explores the environment, and learns object affordances as probabilistic dependencies between actions, object visual properties and observed effects; we use Bayesian Networks to encode this probabilistic model. By making inferences across the learned dependencies a number of cognitive skills are enabled: e.g. i) predicting the effects of an action over an object, or ii) selecting the best action to obtain a desired effect. By exploring object-object interactions the robot can develop the concept of tool (i.e. a handheld object that allows to obtain a desired effect on another object), and eventually use the acquired knowledge to plan sequences of actions to attain a desired goal (i.e. problem solving).
Cloning of a Stretch-inhibitable Nonselective Cation Channel*

A homologue of the capsaicin receptor-nonselective cation channel was cloned from the rat kidney to investigate a mechanosensitive channel. We found this channel to be inactivated by membrane stretch and have designated it stretch-inactivated channel (SIC). SIC encodes a 563-amino acid protein with putative six transmembrane segments. The cDNA was expressed in mammalian cells, and electophysiological studies were performed. SIC-induced large cation currents were found to be regulated by cell volume, with currents being stimulated by cell shrinkage and inhibited by cell swelling. Single channel analysis showed a conductance of 250 pS with cation permeability (PCl/PNa < 0.1), and the channel possessed some of the characteristics of a stretch-inactivated channel in that it was permeable to calcium, sensitive to membrane stretch, and blocked by Gd3+. Therefore, we cloned one of the mechanosensitive cation channels of mammals, which is considered to regulate Ca2+ influx in response to mechanical stress on the cell membrane.
Working memory and everyday conditional reasoning: Retrieval and inhibition of stored counterexamples

Two experiments examined the contribution of working memory (WM) to the retrieval and inhibition of background knowledge about counterexamples (alternatives and disablers, Cummins, 1995) during conditional reasoning. Experiment 1 presented a conditional reasoning task with everyday, causal conditionals to a group of people with high and low WM spans. High spans rejected the logically invalid AC and DA inferences to a greater extent than low spans, whereas low spans accepted the logically valid MP and MT inferences less frequently than high spans. In Experiment 2, an executive-attention-demanding secondary task was imposed during the reasoning task. Findings corroborate that WM resources are used for retrieval of stored counterexamples and that people with high WM spans will use WM resources to inhibit the counterexample activation when the type of counterexample conflicts with the logical validity of the reasoning problem.
Estatística Multivariada Aplicada ao Estudo da Qualidade do Ar

Abstract The air quality study in a city involves different aspects, such as emissions, physical and chemical processes, meteorol-ogy and topography, making this study highly nonlinear. In particular, Brazilian cities experience a very different mo-saic from world scenario, due to the use of different fuels by our vehicular fleet. In this study, it were used a data setcollected by an air quality monitoring station in the city of Rio de Janeiro, at Gavea district, between July and October of2011,inatotalof2,240hourlyaveragedataset.Thesedataweretreatedstatistically,usingadescriptiveandmultivariateapproach in order to elucidate the correlation between the variables involved. Multivariate statistical used the principalcomponent analysis and cluster Euclidean with Ward criteria. The results showed a strong correlation between the pri-mary pollutants nitrogen monoxide and carbon monoxide (0.71), showing that they have the same vehicular origin andnitrogen monoxide and ozone (0.66) and this with solar radiation (0.41), corroborating the photochemical formation ofozone. Other correlation is with temperature and relative humidity (0.64) and that ozone has a contribution of neighbor-ing localities, due to the dependence of this with the wind speed (0.59).
A Method for Obtaining the Optimum Sectionalization of the RMLD Algorithm for Non-Linear Rectangular Codes

The hydrogenation of olefin polymers and especially diene polymers can be carried out effectively in a fixed bed mode of operation, preferably with up-flow of polymer solution, using a heterogeneous catalyst containing an active hydrogenation metal in which the catalyst support has defined surface area and pore volume characteristics and the hydrogenation metal or metal compound is incorporated into the support by impregnation with a non-aqueous solution of the metal or metal compound.
Thermal test vehicle for the validation of thermal modelling of hot spot dissipation in 3D stacked ICs

3D stacking of dies is a promising technique to allow miniaturization and performance enhancement of electronic systems. The complexity of the interconnection structures, combined with the reduced thermal spreading in the thinned dies and the poorly thermally conductive adhesives complicate the thermal behaviour of a stacked die structure. The same dissipation will lead to higher temperatures and a more pronounced temperature peak in a stacked die package compared to a single die package. Therefore, the thermal behaviour in a 3D-IC needs to be studied thoroughly. In this paper, a test vehicle to assess power dissipation in multiple hot spots and the thermal impact of TSVs, is presented. In this test vehicle, a stack of test chips with integrated heaters and temperature sensors is used to evaluate the steady state and transient temperature profile of multiple hot spots. With this experimental set-up, the thermal modelling of the 3D stacks is validated.
PyTrx: A Python toolbox for deriving velocities, surface areas and
line measurements from oblique imagery in glacial environments

Abstract. Terrestrial time-lapse photogrammetry is a rapidly growing method for deriving measurements from glacial environments because it provides high spatio-temporal resolution records of change. However, glacial photogrammetry toolboxes are limited currently. Without prior knowledge in photogrammetry and computer coding, they are used primarily to calculate ice flow velocities or to serve as qualitative records. PyTrx (available at https://github.com/PennyHow/PyTrx) is presented here as a Python-alternative toolbox to widen the range of photogrammetry toolboxes on offer to the glaciology community. The toolbox holds core photogrammetric functions for point seeding, feature-tracking, image registration, and georectification (using a planar projective transformation model). In addition, PyTrx facilitates areal and line measurements, which can be detected from imagery using either an automated or manual approach. Examples of PyTrx's applications are demonstrated using time-lapse imagery from Kronebreen and Tunabreen, two tidewater glaciers in Svalbard. Products from these applications include ice flow velocities, surface areas of supraglacial lakes and meltwater plumes, and glacier terminus profiles.
Risk Assessment and Trust in Services Computing: Applications and Experience

Service-orientation is effective at managing complexity and dynamicity at a programmatic level, but there is still much work to be done in understanding and improving the trust that users place in a system's outputs, and the extent to which they understand the associated risks of decisions recommended by a system. This is crucial if we are to improve the uptake and real-world effectiveness of service-based decision-support systems whilst also reducing the risks (both perceived and actual) of using such systems. This paper presents the current progress of the STRAPP project, which is designing and engineering novel trust and risk assessment mechanisms for services computing and applying these to a number of real-world service-based decision-support systems. A new layered architecture model for trust and risk is introduced and described in detail, and we present our state-of-the-art work in risk-assessment, demonstrating the relationship between provenance data and risk via a mathematical model. We then give a detailed description of our latest software demonstrator, integrated into the Rolls-Royce Equipment Health Management system, and comprehensively discuss the lessons we have learnt from developing such a complex, holistic, and applied real-world system. Finally, we describe the future work we plan to complete.
An iterative algorithm learning the maximal margin classi"er

A simple learning algorithm for maximal margin classi"ers (also support vector machines with quadratic cost function) is proposed. We build our iterative algorithm on top of the Schlesinger–Kozinec algorithm (S–K-algorithm) from 1981 which "nds a maximal margin hyperplane with a given precision for separable data. We suggest a generalization of the S–K-algorithm (i) to the non-linear case using kernel functions and (ii) for non-separable data. The requirement in memory storage is linear to the data. This property allows the proposed algorithm to be used for large training problems. The resulting algorithm is simple to implement and as the experiments showed competitive to the state-of-the-art algorithms. The implementation of the algorithm in Matlab is available. We tested the algorithm on the problem aiming at recognition poor quality numerals.
Real-time calculus for scheduling hard real-time systems

This paper establishes a link between three areas, namely Max-Plus Linear System Theory as used for dealing with certain classes of discrete event systems, Network Calculus for establishing time bounds in communication networks, and real-time scheduling. In particular, it is shown that important results from scheduling theory can be easily derived and unified using Max-Plus Algebra. Based on the proposed network theory for real-time systems, the first polynomial algorithm for the feasibility analysis and optimal priority assignment for a general task model is derived.
Dynamic prediction of architectural vulnerability from microarchitectural state

Transient faults due to particle strikes are a key challenge in microprocessor design. Driven by exponentially increasing transistor counts, per-chip faults are a growing burden. To protect against soft errors, redundancy techniques such as redundant multithreading (RMT) are often used. However, these techniques assume that the probability that a structural fault will result in a soft error (i.e., the Architectural Vulnerability Factor (AVF)) is 100 percent, unnecessarily draining processor resources. Due to the high cost of redundancy, there have been efforts to throttle RMT at runtime. To date, these methods have not incorporated an AVF model and therefore tend to be ad hoc. Unfortunately, computing the AVF of complex microprocessor structures (e.g., the ISQ) can be quite involved. To provide probabilistic guarantees about fault tolerance, we have created a rigorous characterization of AVF behavior that can be easily implemented in hardware. We experimentally demonstrate AVF variability within and across the SPEC2000 benchmarks and identify strong correlations between structural AVF values and a small set of processor metrics. Using these simple indicators as predictors, we create a proof-of-concept RMT implementation that demonstrates that AVF prediction can be used to maintain a low fault tolerance level without significant performance impact.
Methods for information server selection

The problem of using a broker to select a subset of available information servers in order to achieve a good trade-off between document retrieval effectiveness and cost is addressed. Server selection methods which are capable of operating in the absence of global information, and where servers have no knowledge of brokers, are investigated. A novel method using Lightweight Probe queries (LWP method) is compared with several methods based on data from past query processing, while Random and Optimal server rankings serve as controls. Methods are evaluated, using TREC data and relevance judgments, by computing ratios, both empirical and ideal, of recall and early precision for the subset versus the complete set of available servers. Estimates are also made of the best-possible performance of each of the methods. LWP and Topic Similarity methods achieved best results, each being capable of retrieving about 60% of the relevant documents for only one-third of the cost of querying all servers. Subject to the applicable cost model, the LWP method is likely to be preferred because it is suited to dynamic environments. The good results obtained with a simple automatic LWP implementation were replicated using different data and a larger set of query topics.
To Analyse the Erosive Potential of Commercially Available Drinks on Dental Enamel and Various Tooth Coloured Restorative Materials - An In-vitro Study.

INTRODUCTION With the enormous change in life style pattern of a common man through the past few decades, there has been proportional variation in the amount and frequency of consumption of drinks. An increased consumption of these drinks will concurrently increase enamel surface roughness by demineralization, resulting in hypersensitivity and elevated caries risk. AIM The present study was designed to evaluate the erosive potential of commercially available drinks on tooth enamel and various tooth coloured restorative materials. MATERIALS AND METHODS Extracted human teeth were taken and divided into four groups i.e. tooth enamel, glass ionomer cement, composite and compomer. Four commercially available drinks were chosen these were Coca -Cola, Nimbooz, Frooti and Yakult. The pH of each drink was measured. Each group was immersed in various experimental drinks for a period of 14 days. The erosive potential of each drink was measured by calculating the change in average surface roughness of these groups after the immersion protocol in various drinks. The data analysis was done by One Way Anova, Post-Hoc Bonferroni, and paired t -test. RESULTS Group II-GIC showed highest values for mean of change in average surface roughness and the values were statistically significant (p<0.001) with tooth enamel, composite and compomer (p=0.002). Coca-cola showed the highest erosive potential and Yakult showed the lowest, there was no statistical significant difference between the results shown by Yakult and Frooti. CONCLUSION Characteristics which may promote erosion of enamel and tooth coloured restorative materials were surface texture of the material and pH of the drinks.
Application of Machine Learning to Classification of Volcanic Deformation in Routinely Generated InSAR Data

Recent improvements in the frequency, type, and availability of satellite images mean it is now feasible to routinely study volcanoes in remote and inaccessible regions, including those with no ground‐based monitoring. In particular, Interferometric Synthetic Aperture Radar data can detect surface deformation, which has a strong statistical link to eruption. However, the data set produced by the recently launched Sentinel‐1 satellite is too large to be manually analyzed on a global basis. In this study, we systematically process >30,000 short‐term interferograms at over 900 volcanoes and apply machine learning algorithms to automatically detect volcanic ground deformation. We use a convolutional neutral network to classify interferometric fringes in wrapped interferograms with no atmospheric corrections. We employ a transfer learning strategy and test a range of pretrained networks, finding that AlexNet is best suited to this task. The positive results are checked by an expert and fed back for model updating. Following training with a combination of both positive and negative examples, this method reduced the number of interferograms to ∼100 which required further inspection, of which at least 39 are considered true positives. We demonstrate that machine learning can efficiently detect large, rapid deformation signals in wrapped interferograms, but further development is required to detect slow or small deformation patterns which do not generate multiple fringes in short duration interferograms. This study is the first to use machine learning approaches for detecting volcanic deformation in large data sets and demonstrates the potential of such techniques for developing alert systems based on satellite imagery.