Miracle-dz/UniFAR-FTUs huggingface dataset:
Sediment release of dissolved organic matter to the oxygen
minimum zone off Peru

The eastern tropical South Pacific (ETSP) represents one of the most productive areas in the ocean that is characterised by a pronounced oxygen minimum zone (OMZ). Particulate organic matter (POM) that sinks out of the euphotic zone is supplied to the anoxic sediments and utilised by microbial communities, and the degradation of POM is associated with the production and reworking of dissolved organic matter (DOM). The release of DOM to the overlying waters may, therefore, represent an important organic matter escape mechanism from remineralisation within sediments but has received little attention in OMZ regions so far. Here, we combine measurements of dissolved organic carbon (DOC) and dissolved organic nitrogen (DON) with DOM optical properties in the form of chromophoric (CDOM) and fluorescent (FDOM) DOM from pore waters and near-bottom waters of the ETSP off Peru. We evaluate diffusion-driven fluxes and net in situ fluxes of DOC and DON to investigate processes affecting DOM cycling at the sediment–water interface along a transect at 12 S. To our knowledge, these are the first data for sediment release of DON and pore water CDOM and FDOM for the ETSP off Peru. Pore water DOC accumulated with increasing sediment depth, suggesting an imbalance between DOM production and remineralisation within sediments. High DON accumulation resulted in very low pore water DOC /DON ratios (≤ 1) which could be caused by an “uncoupling” in DOC and DON remineralisation. Diffusion-driven fluxes of DOC and DON exhibited high spatial variability and ranged from 0.2± 0.1 to 2.5± 1.3 mmolm−2 d−1 and from −0.04± 0.02 to 3.3±1.7 mmolm−2 d−1, respectively. Generally low net in situ DOC and DON fluxes, as well as a steepening of spectral inclination (S) of CDOM and an increase in humic-like DOM at the sediment–water interface over time, indicated active microbial DOM utilisation. The latter may potentially be stimulated by the presence of nitrate (NO−3 ) and nitrite (NO−2 ) in the water column. The microbial DOC utilisation rates, estimated in our study, are potentially sufficient to support denitrification rates of 0.2–1.4 mmolm−2 d−1, suggesting that the sediment release of DOM may on occasion contribute to nitrogen loss processes in the ETSP off Peru.
Self-Paced Walking within a Diverse Topographical Environment Elicits an Appropriate Training Stimulus for Cardiac Rehabilitation Patients

Purpose. To assess the effect of a self-paced walking intervention within a topographically varied outdoor environment on physiological and perceptual markers in cardiac rehabilitation (CR) patients. Methods. Sixteen phase II CR patients completed twelve self-paced one-mile walking sessions over a four-week period within a community-based CR programme. Walking velocity, heart rate (HR), and ratings of perceived exertion (RPE) were reported at eight stages throughout the self-paced walks. Results. The study showed a significant increase in walking velocity from week 1 (~4.5 km/h) to week 4 (~5.1 km/h) of the self-paced walking programme (P < .05). A significantly higher HR was also observed in week 4 (111 ± 13 b·min−1; ~69% of maximal HR) compared to week 1 (106 ± 14 b·min−1; ~65% of maximal HR, P < .001). There were no changes in the average RPE across the course of the 4-week self-paced walking programme (P > .05). Conclusion. A self-paced walking programme may elicit an appropriate training stimulus for CR patients when exercising within a diverse topographical environment. Participants completed a one-mile walk within a shorter period of time and at a higher physiological intensity than that elicited at the onset of the programme, despite no observed changes in participants' subjective perception of exertion.
NADPH Oxidases, Reactive Oxygen Species, and Hypertension

Reactive oxygen species (ROS) influence many physiological processes including host defense, hormone biosynthesis, fertilization, and cellular signaling. Increased ROS production (termed “oxidative stress”) has been implicated in various pathologies, including hypertension, atherosclerosis, diabetes, and chronic kidney disease. A major source for vascular and renal ROS is a family of nonphagocytic NAD(P)H oxidases, including the prototypic Nox2 homolog-based NAD(P)H oxidase, as well as other NAD(P)H oxidases, such as Nox1 and Nox4. Other possible sources include mitochondrial electron transport enzymes, xanthine oxidase, cyclooxygenase, lipoxygenase, and uncoupled nitric oxide synthase. NAD(P)H oxidase-derived ROS plays a physiological role in the regulation of endothelial function and vascular tone and a pathophysiological role in endothelial dysfunction, inflammation, hypertrophy, apoptosis, migration, fibrosis, angiogenesis, and rarefaction, important processes underlying cardiovascular and renal remodeling in hypertension and diabetes. These findings have evoked considerable interest because of the possibilities that therapies against nonphagocytic NAD(P)H oxidase to decrease ROS generation and/or strategies to increase nitric oxide (NO) availability and antioxidants may be useful in minimizing vascular injury and renal dysfunction and thereby prevent or regress target organ damage associated with hypertension and diabetes. Here we highlight current developments in the field of reactive oxygen species and cardiovascular disease, focusing specifically on the recently identified novel Nox family of NAD(P)H oxidases in hypertension. We also discuss the potential role of targeting ROS as a therapeutic possibility in the management of hypertension and cardiovascular disease.
Introduction to OLIN package

Microarray measurements are affected by a variety of systematic experimental errors limiting the accuracy of data produced. Two well-known systematic errors for two-colour arrays are the so-called intensity-dependent and spatial (location-dependent) dye bias. Normalisation aims to correct for systematic errors in microarray data. The common linear (or global) normalisation method often fails to correct for dye bias as this bias is usually not linear. Although non-linear normalisation procedures have been able to reduce the systematic errors, these methods are based on default parameters and leave it to the user to choose “good” parameters. The optimal adjustment of the normalisation models to the data, however, can be crucial for the efficiency of the normalisation process [1]. The OLIN (Optimised Local Intensity-dependent Normalisation) R-package includes two normalisation schemes based on iterative local regression and model selection. Both schemes aim to correct for intensityand location-dependent dye bias in microarray data. For model selection (parameter optimisation), generalized cross-validation (GCV) is used. Additionally, several procedures to assess the efficiencies of normalisation are implemented in the package. A graphical user interface for OLIN has been implemented in the package OLINgui, that can be downloaded from the OLIN webpage (http://itb.biologie.hu-berlin.de/∼futschik/software/R/OLIN ).
Cost-sensitive background subtraction

Foreground and background are treated without distinction at classification stage in most background subtraction algorithms. However, correct classification of foreground is the primary requirement, and thus misclassification costs of the two classes should be different. Based on this fact, we present a new method to introduce cost sensitivity into background subtraction, where a cost matrix is created to represent the costs of misclassification. Some items in the cost matrix are not constants, but functions of foreground occurence at each pixel location. By the use of such non-constant costs, detection rate of foreground is improved while increase of false alarms is prevented at the same time. Experiments demonstrate the effectiveness of the proposed algorithm.
A world of active objects for work and play: the first ten years of lively

The Lively Kernel and the Lively Web represent a continuing effort to realize a creative computing environment in the context of the World Wide Web. We refer to that evolving system simply as "Lively". Lively is a live object computing environment implemented using JavaScript and other techniques available inside the browser. When first built in 2006, it was a grand accomplishment to have created such a system that would run in any web browser. Since that time we have learned a lot about the goals we had, the challenges and opportunities that come with life in the browser, and the exciting possibilities that still lie ahead.
Evaluating Research and Impact: A Bibliometric Analysis of Research by the NIH/NIAID HIV/AIDS Clinical Trials Networks

Evaluative bibliometrics uses advanced techniques to assess the impact of scholarly work in the context of other scientific work and usually compares the relative scientific contributions of research groups or institutions. Using publications from the National Institute of Allergy and Infectious Diseases (NIAID) HIV/AIDS extramural clinical trials networks, we assessed the presence, performance, and impact of papers published in 2006–2008. Through this approach, we sought to expand traditional bibliometric analyses beyond citation counts to include normative comparisons across journals and fields, visualization of co-authorship across the networks, and assess the inclusion of publications in reviews and syntheses. Specifically, we examined the research output of the networks in terms of the a) presence of papers in the scientific journal hierarchy ranked on the basis of journal influence measures, b) performance of publications on traditional bibliometric measures, and c) impact of publications in comparisons with similar publications worldwide, adjusted for journals and fields. We also examined collaboration and interdisciplinarity across the initiative, through network analysis and modeling of co-authorship patterns. Finally, we explored the uptake of network produced publications in research reviews and syntheses. Overall, the results suggest the networks are producing highly recognized work, engaging in extensive interdisciplinary collaborations, and having an impact across several areas of HIV-related science. The strengths and limitations of the approach for evaluation and monitoring research initiatives are discussed.
Shaping spoken input in user-initiative systems

A spoken dialog system performs best when users speak within the grammar that the system understands. We conducted a simple study to investigate how easily users can be persuaded to speak to a system using a restricted, less-thannatural-language input style. In a Wizard-of-Oz setting, users of a spoken dialogue system for information access were given brief instructions to “speak simply” to the system. During their interactions, conversational or complex input was rejected by the system while simpler, “just-the-facts” input was accepted. We found that all users were able to adapt their language to successfully complete tasks, and participants’ post-experiment comments showed that they were consistently mindful of the form of acceptable input. These results will support further investigation into more precise shaping of user input, leading to more effective and efficient human-machine speech interaction.
Song Diversity Predicts the Viability of Fragmented Bird Populations

In the global scenario of increasing habitat fragmentation, finding appropriate indicators of population viability is a priority for conservation. We explored the potential of learned behaviours, specifically acoustic signals, to predict the persistence over time of fragmented bird populations. We found an association between male song diversity and the annual rate of population change, population productivity and population size, resulting in birds singing poor repertoires in populations more prone to extinction. This is the first demonstration that population viability can be predicted by a cultural trait (acquired via social learning). Our results emphasise that cultural attributes can reflect not only individual-level characteristics, but also the emergent population-level properties. This opens the way to the study of animal cultural diversity in the increasingly common human-altered landscapes.
Comparison of alignment templates and maximum entropy models for natural language understanding

In this paper we compare two approaches to natural language understanding (NLU). The first approach is derived from the field of statistical machine translation (MT), whereas the other uses the maximum entropy (ME) framework. Starting with an annotated corpus, we describe the problem of NLU as a translation from a source sentence to a formal language target sentence. We mainly focus on the quality of the different alignment and ME models and show that the direct ME approach outperforms the alignment templates method.
Towards Odalic, a Semantic Table Interpretation Tool in the ADEQUATe Project

The goal of the ADEQUATe project is to assess and improve quality of the (tabular) open data being published at two Austrian open data portals – https://www.data. gv.at and https://www.opendataportal. at. The goal of the quality improvement technique described in this paper is to semantically interpret such tabular data and publish them as Linked Data; this basically means to (1) classify columns of the input data using Linked Data vocabularies, (2) link cell values of the input data against Linked Data entities, (3) discover relations among the columns of the input data by searching for evidences of such relations among Linked Data sources, and (4) export such semantically interpreted data as RDF/Linked Data. In this paper, we describe limitations of TableMiner+, one of the tools for semantic table interpretation, with respect to our needs in the ADEQUATe project. Furthermore, we present Odalic, a tool for (semi)automatic semantic table interpretation and Linked Data publishing and describe how it addresses these limitation. We describe lessons learned using Odalic in the ADEQUATe project and also future work planned.
Performance Guarantees for Distributed Reachability Queries

In the real world a graph is often fragmented and distributed across different sites. This highlights the need for evaluating queries on distributed graphs. This paper proposes distributed evaluation algorithms for three classes of queries: reachability for determining whether one node can reach another, bounded reachability for deciding whether there exists a path of a bounded length between a pair of nodes, and regular reachability for checking whether there exists a path connecting two nodes such that the node labels on the path form a string in a given regular expression. We develop these algorithms based on partial evaluation, to explore parallel computation. When evaluating a query Q on a distributed graph G, we show that these algorithms possess the following performance guarantees, no matter how G is fragmented and distributed: (1) each site is visited only once; (2) the total network traffic is determined by the size of Q and the fragmentation of G, independent of the size of G; and (3) the response time is decided by the largest fragment of G rather than the entire G. In addition, we show that these algorithms can be readily implemented in the MapReduce framework. Using synthetic and real-life data, we experimentally verify that these algorithms are scalable on large graphs, regardless of how the graphs are distributed.
Approximate k-flat Nearest Neighbor Search

Let k ≥ 0 be an integer. In the approximate k-flat nearest neighbor (k-ANN) problem, we are given a set P ⊂ Rd of n points in d-dimensional space and a fixed approximation factor c > 1. Our goal is to preprocess P so that we can efficiently answer approximate k-flat nearest neighbor queries: given a k-flat F, find a point in P whose distance to F is within a factor c of the distance between F and the closest point in P. The case k = 0 corresponds to the well-studied approximate nearest neighbor problem, for which a plethora of results are known, both in low and high dimensions. The case k = 1 is called approximate line nearest neighbor. In this case, we are aware of only one provably efficient data structure, due to Andoni, Indyk, Krauthgamer, and Nguyen (AIKN) [2]. For k ≥ 2, we know of no previous results. We present the first efficient data structure that can handle approximate nearest neighbor queries for arbitrary k. We use a data structure for 0-ANN-queries as a black box, and the performance depends on the parameters of the 0-ANN solution: suppose we have a 0-ANN structure with query time O(nρ) and space requirement O(n1+σ), for ρ, σ > 0. Then we can answer k-ANN queries in time O(nk/(k + 1 - ρ) + t)$ and space O(n1+σ k/(k + 1 - ρ) + n logO(1/t) n). Here, t > 0 is an arbitrary constant and the O-notation hides exponential factors in k, 1/t, and c and polynomials in d. Our approach generalizes the techniques of AIKN for 1-ANN: we partition P into clusters of increasing radius, and we build a low-dimensional data structure for a random projection of P. Given a query flat F, the query can be answered directly in clusters whose radius is "small" compared to d(F, P) using a grid. For the remaining points, the low dimensional approximation turns out to be precise enough. Our new data structures also give an improvement in the space requirement over the previous result for 1-ANN: we can achieve near-linear space and sublinear query time, a further step towards practical applications where space constitutes the bottleneck.
Efficient algorithms for minimizing tree pattern queries

We consider the problem of minimizing tree pattern queries (TPQ) that arise in XML and in LDAP-style network directories. In [Minimization of Tree Pattern Queries, <i>Proc. ACM SIGMOD Intl. Conf. Management of Data,</i> 2001, pp. 497-508], Amer-Yahia, Cho, Lakshmanan and Srivastava presented an <i>O</i>(<i>n</i><sup>4</sup>) algorithm for minimizing TPQs in the absence of integrity constraints (Case 1); <i>n</i> is the number of nodes in the query. Then they considered the problem of minimizing TPQs in the presence of three kinds of integrity constraints: required-child, required-descendant and subtype (Case 2). They presented an <i>O</i>(<i>n</i><sup>6</sup>) algorithm for minimizing TPQs in the presence of only required-child and required-descendant constraints (i.e., no subtypes allowed; Case 3). We present <i>O</i>(<i>n</i><sup>2</sup>), <i>O</i>(<i>n</i><sup>4</sup>) and <i>O</i>(<i>n</i><sup>2</sup>) algorithms for minimizing TPQs in these three cases, respectively, based on the concept of graph simulation. We believe that our <i>O</i>(<i>n</i><sup>2</sup>) algorithms for Cases 1 and 3 are runtime optimal.
Consciousness as Integrated Information: a Provisional Manifesto

The integrated information theory (IIT) starts from phenomenology and makes use of thought experiments to claim that consciousness is integrated information. Specifically: (i) the quantity of consciousness corresponds to the amount of integrated information generated by a complex of elements; (ii) the quality of experience is specified by the set of informational relationships generated within that complex. Integrated information (Φ) is defined as the amount of information generated by a complex of elements, above and beyond the information generated by its parts. Qualia space (Q) is a space where each axis represents a possible state of the complex, each point is a probability distribution of its states, and arrows between points represent the informational relationships among its elements generated by causal mechanisms (connections). Together, the set of informational relationships within a complex constitute a shape in Q that completely and univocally specifies a particular experience. Several observations concerning the neural substrate of consciousness fall naturally into place within the IIT framework. Among them are the association of consciousness with certain neural systems rather than with others; the fact that neural processes underlying consciousness can influence or be influenced by neural processes that remain unconscious; the reduction of consciousness during dreamless sleep and generalized seizures; and the distinct role of different cortical architectures in affecting the quality of experience. Equating consciousness with integrated information carries several implications for our view of nature.
Velocity of Shortening of Unloaded Heart Muscle and the Length‐Tension Relation

A new technique permits removal of the resting force from the muscle during the course of shortening and study of the maximum velocity of shortening with almost zero load at physiological initial muscle lengths. This unloading has been performed as a sudden unloading to zero total load (zero load clamping), or according to the resting length-tension relations, or according to an arbitrary unloading intermediate between both former modes of unloading. Using these various modes of unloading to zero load during shortening, it has been demonstrated that the maximum velocity of shortening Vmax remains constant between maximum length (lmax) and a length 12.5% shorter than lmax. This unique force (zero load)-velocity (Vmax)-length (up to 12.5% below lmax) relation is independent of die initial muscle length over this range of lengths, independent of the time after the stimulus over the largest portion of the shortening phase, and independent of the sequence of length change and mode of unloading through which it arrived at zero load within this range of lengths.
Deep Optical Galaxy Counts with the Keck Telescope

We present faint galaxy counts from deep VRI images obtained with the Keck Telescope. These images reach R ~ 27 in median seeing FWHM ~ 0. ″5-0. ″6, and we detect a integrated galaxy number density of 7 × 105 deg-2, equivalent to 3 × 1010 galaxies in the observable universe. In addition we present median galaxy colors as a function of magnitude; bluing trends are visible in all colors to R ~ 24.5. Fainter than R ~ 24.5, however, the typical V - R color becomes redder again, V - I remains constant, and R - I becomes yet bluer. These trends are consistent with the VRI count slopes, implying a decrease in the V slope at the faintest levels, which our data support. Taking advantage of our good seeing we also present median half-light radii for faint galaxies; these show a steady decline at fainter magnitudes, leading to an intrinsic half-light radius of ~0. ″2 for a typical R ~ 26 galaxy. Irrespective of the redshift distribution, the extremely high galaxy surface densities and their small intrinsic sizes are consistent with a scenario in which the majority of the very faint field population are dwarf galaxies or subgalactic units.
Punjabi Poetry Classification: The Test of 10 Machine Learning Algorithms

Literature of country represents the prosperity of that country. India, being a multilingual country, is having a rich heritage and literature. In order to retrieve literature pieces easily, it must be classified. In this research work, vocabulary-content based classification of Punjabi poetry is done. 4 different poetry categories are populated with 240 poetries (with 60 poems in each category). These 240 poetry documents are passed through typical NLP text classification phases like Sentence Splitting, Tokenization and Bag-of-Words (BOW) representation, finally yielding to their Vector Space Model (VSM) representation. Total 9867 unique words extracted from last step are used for building the different machine learning models. For the first time in research community, 10 different machine learning algorithms are trained and tested for any Indian language, using weka, with an aim to find the most suitable algorithm. Results for Punjabi poetry classification revealed that 4 machine learning algorithms namely, Hyperpipes (HP), K- nearest neighbour (KNN), Naive Bayes (NB) and Support Vector Machine (SVM) with an accuracy of 50.63 %, 52.92 %, 52.75 % and 58.79 % respectively, outperformed all other machine learning algorithms under the test.