Miracle-dz/UniFAR-FTUs huggingface dataset:
Isotopic composition of suspended particulate nitrogen (δ15Nsus) in surface waters of the Atlantic Ocean from 50°N to 50°S

Isotopic composition of suspended particulate nitrogen (δ15Nsus) has been studied in near‐surface waters along a transect extending from 50°N to 50°S in the Atlantic Ocean. The δ15Nsus values ranged from −0.8 to +5.4‰ and showed a significant correlation with depth of nitracline (δ15Nsus = −0.047 × + 5.706; R2 = 0.81, n = 15) in nutrients‐depleted regions (45°N–40°S). This inverse relationship implied that higher uptake of new NO3− during mesotrophic conditions, characterized by shallower derived relatively enriched δ15Nsus. On the other hand, lower 15N signature of the remineralized NH4+ would be mainly imprinted on particulate nitrogen pool in the oligotrophic regions characterized by deep The observed inverse relationship can be modeled by a simple isotopic mass balance equation concerning with the N utilizability of phytoplankton (in terms of f‐ratio). The model results that the variations in δ15Nsus reflect the regional differences in nitrogen cycle in the upper ocean. In the region between equator and 15°N, where N2‐fixing cyanobacteria (Trichodesmium spp.) were found, the measured δ15Nsus values were about 2‰ lower than those expected from the regression with the indicating that over 38% of the suspended particulate nitrogen came through N2‐fixation in this region. Regional variations in δ15Nsus in surface waters also correlated with those in water column productivity implying that the δ15Nsus signature can provide an estimate of in situ algal production.
Algorithms for Face Recognition

Over the last ten years, face recognition has become a specialized applications area within the field of computer vision. Sophisticated commercial systems have been developed that achieve high recognition rates. The goal of this report is to compare three mathematical algorithms on the basis of a face recognition task. The first technique is principal component analysis (PCA), a well-known “baseline” for projection techniques. The second technique is independent component analysis (ICA), a newer method that produces spatially localized and statistically independent basis vectors. In the third technique i.e. LDA, the goal is to find an efficient and interesting way to represent the face vector space by taking into account the separate class statistics. Keywords—PCA; ICA; LDA; Biometrics; Face recognition.
Experimental dissection of flagellar surface motility in chlamydomonas

Experiments have explored the possible relationships between the flagellar surface motility of chlamydomonas, visualized as translocation of polystyrene beads by paralyzed (pf) mutants (Bloodgood, 1977, J. Cell Biol. 15:983-989), and the capacity of gametic flagella to participate in the mating reaction. While vegetative and gametic flagella bind beads with equal efficiencies and are capable of transporting them along entire flagellar lengths, beads on vegetative flagella are primarily associated with the proximal half of the flagella whereas those of gametic flagella exhibit no such preference. This difference may relate to the "tipping" response of gametes during sexual flagellar agglutination (Goodenough and Jurivich, 1978, J. Cell Biol. 79:680-693). Colchicine, vinblastine, chymotrypsin, cytochalasins B and D, and anti-β-tubulin antiserum are all able to inhibit the binding of beads to the flagellar suface. Trysin digestion and an antiserum directed against whole chlamydomonas flagella have no effect on the ability of flagella to bind beads, but the beads remain immobile. These results suggest that at least two flagellar activities participate in surface motility: (a) bead binding, which may involve a tubulin-like component at the flagellar surface; and (b) bead translocation, which may depend on a second component (e.g. an ATPase) of the flagellar surface. Surface motility is shown to be distinct from gametic adhesiveness per se, but it may participate in concentrating dispersed agglutinins, in driving them toward the flagellar tips, and/or in generating a signal-to-fuse from the flagellar tips to the cell body. Directly supporting these concepts is the observation that bound beads remain immobilized at the flagellar tips during the "tip-locking" stage of pf x pf matings, and the observation that bound ligands such as antibody fail to be tipped by trypsinized flagella.
Lung CD25 CD4 Regulatory T Cells Suppress Type 2 Immune Responses But Not Bronchial Hyperreactivity1

To study the effects of chronic Ag deposition in the airway mucosa on CD4+ T cell priming and subsequent airway disease, transgenic mice were generated that expressed OVA under the control of the surfactant protein C promoter. CD4 T cells from these mice were tolerant to OVA but this was overcome among spleen CD4 T cells by crossing to OVA-specific DO11.10 TCR-transgenic mice. Lungs from the double-transgenic mice developed lymphocytic infiltrates and modest mucus cell hyperplasia. Infiltrating cells were unaffected by the absence of either Rag-1 or Stat6, although the latter deficiency led to the disappearance of mucus. In the lung of double-transgenic mice, a large number of Ag-specific CD4 T cells expressed CD25 and functioned as regulatory T cells. The CD25+ CD4 T cells suppressed proliferation of CD25− CD4 T cells in vitro and inhibited type 2 immune responses induced by aerosolized Ags in vivo. Despite their ability to suppress allergic type 2 immunity in the airways, however, CD25+ CD4 regulatory T cells had no effect on the development of bronchial hyperreactivity.
A butterfly subdivision scheme for surface interpolation with tension control

A new interpolatory subdivision scheme for surface design is presented. The new scheme is designed for a general triangulation of control points and has a tension parameter that provides design flexibility. The resulting limit surface is C1 for a specified range of the tension parameter, with a few exceptions. Application of the butterfly scheme and the role of the tension parameter are demonstrated by several examples.
Enrichment of a Nigerian chromite ore for metallurgical application by dense medium flotation and magnetic separation

This study, focused on the beneficiation of a Nigerian complex chromite ore sourced from Tunga-Kaduka, Anka Local Government of Zamfara State, Nigeria, assaying 45.85% Cr2O4 and 54.15% mineral impurities, was enriched concurrently through sink floatation and magnetic separation techniques. The chromite ore initially analyzed to contain silicate impurities was found not suitable for metallurgical purposes. Thus, enrichment was examined through gravity separation studies using organic liquid with different specific gravities at 2.8, 3.3, and 4.0. The separation of chromite ore with lowest particle size fraction was done using Mozley mineral separator followed by the magnetic separation of the sink product by magnetic separator. The results obtained revealed about 77% of the total material containing 300 μm particle size, 52% ˂ 212 μm and 17% below 75 μm. Subsequent analysis of the beneficiated ore was carried out by wet chemical analysis and atomic absorption spectrophotometer. The results showed that Cr2O4 content increased to 78.34% from initial 45.83% with maximum Cr:Fe ratio of 3.2:1, representing 84.27% of chromium metal present in the ore. The enrichment of Cr2O4 obtained in this study could be found metallurgically applicable in the electro-deposition and ferro-chromium alloy production practices.
Higher order saddlepoint approximations in the Vasicek portfolio credit loss model

This paper utilizes the saddlepoint approximation as an efficient tool to estimate the portfolio credit loss distribution in the Vasicek model. Value at Risk (VaR), the risk measure chosen in the Basel II Accord for the evaluation of capital requirement, can then be found by inverting the loss distribution. VaR Contribution (VaRC), Expected Shortfall (ES) and ES Contribution (ESC) can all be calculated accurately. Saddlepoint approximation is well known to provide good approximations to very small tail probabilities, which makes it a very suitable technique in the context of portfolio credit loss. The portfolio credit model we employ is the Vasicek one factor model, which has an analytical solution if the portfolio is well diversified. The Vasicek asymptotic formula however fails when the portfolio is dominated by a few loans. We show that saddlepoint approximation is able to handle such exposure concentration. We also point out that the saddlepoint approximation technique can be readily applied to more general Bernoulli mixture models (possibly multi-factor). It can further handle portfolios with random LGD.
Personalized search based on micro-blogging social networks

In todays life, everybody who knows the Internet is familiar with web-based search engines and micro-blogging social networks. People from all over the world are using search engines as well as sending a huge amount of text messages to micro-blogging social networks in the daily life. This work addresses the problem of using micro-blogging social networks to personalize search engine results. We introduce a framework which combines the similarity of results fetched from a non-personalized search engine and the users's interests extracted from the micro-blogging social networks. We used the Twitter social network as the experimental domain to test the performance of proposed algorithms and evaluate the results by applying the algorithms on some of the well-known users who we know their interests.
TGSum: Build Tweet Guided Multi-Document Summarization Dataset

The development of summarization research has been significantly hampered by the costly acquisition of reference summaries. This paper proposes an effective way to automatically collect large scales of news-related multi-document summaries with reference to social media's reactions. We utilize two types of social labels in tweets, i.e., hashtags and hyper-links. Hashtags are used to cluster documents into different topic sets. Also, a tweet with a hyper-link often highlights certain key points of the corresponding document. We synthesize a linked document cluster to form a reference summary which can cover most key points. To this aim, we adopt the ROUGE metrics to measure the coverage ratio, and develop an Integer Linear Programming solution to discover the sentence set reaching the upper bound of ROUGE. Since we allow summary sentences to be selected from both documents and high-quality tweets, the generated reference summaries could be abstractive. Both informativeness and readability of the collected summaries are verified by manual judgment. In addition, we train a Support Vector Regression summarizer on DUC generic multi-document summarization benchmarks. With the collected data as extra training resource, the performance of the summarizer improves a lot on all the test sets. We release this dataset for further research.
The iterative demodulation and decoding algorithm for convolutional coded GMSK signals over mobile satellite channel

A new simple iterative demodulation and decoding algorithm for convolutional coded GMSK signals, which is suitable for mobile satellite channel is presented. Demodulation is simplified through the approximation of GMSK as pulse amplitude modulated (PAM) signal, leading to a representation as the inner convolutional code of a serially concatenated convolutional code (SCCC). In addition, based on quasi-coherent demodulation of GMSK signals, an iterative channel estimation method, which can improve bit error rate (BER) performance of proposed coded GMSK signal transmission scheme dramatically, was expressed. Numerous simulation results show that the proposed algorithm can largely reduce the implementation complexity and have significant performance.
2 Related Work 2 . 1 Web Browsing Quality

Web browsing has emerged as one of the most important end user services carried on today’s networks. However, approaches for determining the Quality of Experience of end user sessions are immature, due to the myriad of factors that affect user perception of web browsing. This paper describes initial work towards a framework for determining end user service quality. It describes an approach for measuring the performance of web browsing sessions and presents measurement results obtained using that approach.
Inverse Mutations: Making the Evolutionary-Gradient-Search Procedure Noise Robust

Recent advances in the theory of evolutionary algorithms have indicated that a hybrid method known as the evolutionary-gradient-search procedure yields superior performance in comparison to contemporary evolution strategies. But the theoretical analysis also indicates a noticeable performance loss in the presence of noise (i.e., noisy fitness evaluations). This paper aims at understanding the reasons for this observable performance loss. It also proposes some modifications, called inverse mutations, to make the process of estimating the gradient direction more noise robust.
Validation of stimulus for EEG signal based cognitive load analysis

Cognitive load is defined as the mental workload imparted on brain while doing a task. The amount of cognitive load experienced depends on individual's ability of perception, assimilation and response to a task. Real-time measurement of the level of cognitive load using low cost Electroencephalogram (EEG) signal enables understanding of personal cognitive skills. In this paper, we propose a methodology of selecting a reference task whose bio-markers closely match with a given task while probing different cognitive abilities. The benefit of this approach is to have a limited set of training models for the reference tasks related to various cognitive categories and use the same for a variety of unknown tasks. Experiment is performed for two levels of cognitive load with three different tasks namely Stroop color task, logical reasoning task and usage of on-screen keyboards. The training models of the reference tasks, selected by cluster analysis of low and high cognitive levels are used to evaluate an unknown task. Experimental results indicate that the Stroop is a better reference for On-Screen keyboard test compared to the Logical reasoning test. Support vector machine (SVM) and principal component analysis (PCA) followed by SVM (PCA-SVM) are used as the classifiers for the testing.
Transcranial sonography as a diagnostic tool for Parkinson's disease: a pilot study in the city of Rio de Janeiro, Brazil.

UNLABELLED In Brazil there is no systematic study on Transcranial Sonography (TCS), a neuroimaging method that depicts echogenic deep brain structures using ultrasound. OBJECTIVE To establish the percentage of subjects with permissive temporal windows and to address the ability of TCS of the substantia nigra (SN) to distinguish parkinsonian patients in a Brazilian sample. METHOD We performed TCS using the Acuson X300 (Siemens, Germany) in 37 individuals: 23 with Parkinson's disease (PD) and 14 healthy controls. RESULTS 10.8% of subjects had insufficient temporal acoustic bone windows. SN echogenic areas were larger in patients (mean ± SD, 0.31 ± 0.08 cm(2)) compared to controls (mean ± SD, 0.17 ± 0.02 cm(2)). TCS accurately identified 88.2% of PD patients. CONCLUSION A large proportion of Brazilians seem to be eligible for TCS. An expressive number of PD patients could be diagnosed by TCS based on an expanded SN echogenic area. However, the current data is preliminary and must be corroborated by larger studies.
An Unbiased cDNA Library Prepared from Isolated Aplysia Sensory Neuron Processes Is Enriched for Cytoskeletal and Translational mRNAs

Local protein synthesis is required for long-lasting synapse-specific plasticity in cultured Aplysia sensorimotor synapses. To identify synaptically localized mRNAs, we prepared a cDNA library from isolated sensory neurites. By sequence analysis, we estimate that the library contains 263 distinct mRNAs, with 98 of these mRNAs constituting 70% of all clones. The localized transcripts are enriched for mRNAs encoding cytoskeletal elements and components of the translational machinery. In situ hybridization confirms that the mRNAs for at least eight of these transcripts are present in distal neurites. Immunocytochemistry reveals that serotonin regulates the translation of one of the localized mRNAs, that encoding α1-tubulin. Our identification of mRNAs encoding cytoskeletal elements suggests that local protein synthesis is required for the growth of new synaptic connections associated with persistent synaptic strengthening. Our finding of mRNAs encoding components of the translational machinery suggests that local protein synthesis serves to increase the translational capacity of synapses.
Is quantification of bolus tracking MRI reliable without deconvolution?

Bolus tracking data obtained with paramagnetic intravascular tracers are commonly analyzed and quantified by the direct measurement of properties of the tissue concentration‐time curve (e.g., time to peak (TTP)). The measurement of these “summary parameters” is used as an accessible alternative approach to the complex deconvolution procedure, and provides indirect measures of perfusion. However, summary parameters do not take into account differences in arterial input functions (AIFs) or residue functions (R(t)) between patients or studies. Simulations were performed to assess the variability of summary parameters over a realistic range of AIFs and for differing R(t), to establish whether they can be used as reliable measures of tissue perfusion status. Results showed that the value of each summary parameter investigated is highly dependent upon both the AIF and R(t). The referencing of summary parameters to their corresponding value in the AIF or in normal tissue is a method commonly used to normalize results, but this approach did not lead to any measures that were independent of both the AIF and R(t) in this study. The results presented here show that the use of summary parameters requires considerable caution, since tissue or patient types can easily be incorrectly classified due to the effect of variations in patient AIF and R(t). Magn Reson Med 47:61–67, 2002. © 2002 Wiley‐Liss, Inc.
Creating Picture Legends for Group Photos

Group photos are one of the most common types of digital images found in personal image collections and on social networks. One typical post‐processing task for group photos is to produce a key or legend to identify the people in the photo. This is most often done using simple bounding boxes. A more professional approach is to create a picture legend that uses either a full or partial silhouette to identify the individuals. This paper introduces an efficient method for producing picture legends for group photos. Our approach combines face detection with human shape priors into an interactive selection framework to allow users to quickly segment the individuals in a group photo. Our results are better than those obtained by general selection tools and can be produced in a fraction of the time.
Handwritten Bangla numeral recognition using Local Binary Pattern

Local Binary Pattern (LBP) is a simple yet robust texture descriptor that has been widely used in many computer vision applications including face recognition. In this paper, we exploit LBP for handwritten Bangla numeral recognition. We classify Bangla digits from their LBP histograms using K Nearest Neighbors (KNN) classifier. The performance of three different variations of LBP - the basic LBP, the uniform LBP and the simplified LBP was investigated. The proposed OCR system was evaluated on the off-line handwritten Bangla numeral database CMATERdb 3.1. 1, and achieved an excellent accuracy of 96:7% character recognition rate.
Working Locally Thinking Globally - Part II: Stability and Algorithms for Convolutional Sparse Coding

The convolutional sparse model has recently gained increasing attention in the signal and image processing communities, and several methods have been proposed for solving the pursuit problem emerging from it -- in particular its convex relaxation, Basis Pursuit. In the first of this two-part work, we have provided a theoretical back-bone for this model, providing guarantees for the uniqueness of the sparsest solution and for the success of pursuit algorithms by introducing the notion of stripe sparsity and other related measures. Herein, we extend the analysis to a noisy regime, thereby considering signal perturbations and model deviations. We address questions of stability of the sparsest solutions and the success of pursuit algorithms, both greedy and convex. Classical definitions such as the RIP are generalized to the convolutional model, and existing notions such as the ERC are connected to our setting. On the algorithmic side, we demonstrate how to solve the global pursuit problem by using simple local processing, thus offering a first of its kind bridge between global modeling of signals and their patch-based local treatment.
Mining web access patterns with first-occurrence linked WAP-trees

In this paper, we describe the concept of firstoccurrence and present a web access pattern mining algorithm based on it using a novel first-occurrence linked WAP-tree (FLWAP-tree). The first-occurrences of all symbols in the base WAP-tree of the database can be found by a pre-order traversal of a portion of the WAP-tree. The frequent patterns and their projection databases can be found quickly by following the first-occurrence links rather than following the links of the whole WAP-tree as in the PLWAP-tree mining [1]. The performance evaluation shows that our FLWAPtree mining outperforms the PLWAP-tree mining [1] consistently and significantly.
A trend-preserving bias correction – the ISI-MIP approach

Abstract. Statistical bias correction is commonly applied within climate impact modelling to correct climate model data for systematic deviations of the simulated historical data from observations. Methods are based on transfer functions generated to map the distribution of the simulated historical data to that of the observations. Those are subsequently applied to correct the future projections. Here, we present the bias correction method that was developed within ISI-MIP, the first Inter-Sectoral Impact Model Intercomparison Project. ISI-MIP is designed to synthesise impact projections in the agriculture, water, biome, health, and infrastructure sectors at different levels of global warming. Bias-corrected climate data that are used as input for the impact simulations could be only provided over land areas. To ensure consistency with the global (land + ocean) temperature information the bias correction method has to preserve the warming signal. Here we present the applied method that preserves the absolute changes in monthly temperature, and relative changes in monthly values of precipitation and the other variables needed for ISI-MIP. The proposed methodology represents a modification of the transfer function approach applied in the Water Model Intercomparison Project (Water-MIP). Correction of the monthly mean is followed by correction of the daily variability about the monthly mean. Besides the general idea and technical details of the ISI-MIP method, we show and discuss the potential and limitations of the applied bias correction. In particular, while the trend and the long-term mean are well represented, limitations with regards to the adjustment of the variability persist which may affect, e.g. small scale features or extremes.