Miracle-dz/UniFAR-FTUs huggingface dataset:
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Consider a piezoelectric transducer that is attached to the back of a plate-like structure, such as is the case for guided wave structural health monitoring. This transducer may be excited to produce guided waves that propagate outward from the source through the structure. These waves interact with impedance discontinuities in the structure at defects, bond lines, plate boundaries, and other geometrical features. These propagating waves may be measured on the surface of the specimen using either a scanning laser vibrometer or a scanning air coupled transducer. When full wavefield data are obtained by recording complete waveforms on a spatial grid, these measurements contain a wealth of information that can be used for identification, localization and characterization of damage. Results are presented here from a study that uses full acoustic wavefield images of propagating guided waves to detect and size different defect types in aluminum plate specimens. Several detection and sizing algorithms have previously been considered, including frequency domain analysis, incident wave removal, and acoustic wave crest analysis. The acoustic wave crest analysis method, which was developed by the authors to study corrosion type defects in aluminum plates, is applied here to analyze wavefield interactions with fatigue cracks in an aluminum plate specimen. The methodology demonstrated here on aluminum plate specimens is appropriate for application to critical metallic structural components as part of an integrated health management system.
Table of Contents Distributional Semantics from Text and Images Reranking Bilingually Extracted Paraphrases Using Monolingual Distributional Similarity Encoding Syntactic Dependencies by Vector Permutation Workshop Program Attribute-related Meaning Representations for Adjective- Noun Phrases in a Si

ii Introduction GEMS 2011 — GEometrical Models of Natural Language Semantics — is the third instalment in a successful series of workshops on distributional models of meaning. Since their earliest application in information retrieval, these models have become omnipresent in contemporary computational linguistics and neighboring fields. Different types of distributional models have been introduced — from the relatively simple bag-of-word, document-based and syntax-based techniques to the statistically more advanced topic models. In the field of lexical semantics, their direct applications include the construction of lexical taxonomies, the recognition of textual entailment, word sense discrimination and disambiguation, cognitive modeling, etc. Moreover, other areas of NLP, like parsing and Machine Translation, have found they can indirectly benefit from the ability of distributional models to generalize from a limited training set to unseen, but semantically similar, words. The growth of distributional semantics, however, is not without its problems. The aim of GEMS is to address two orthogonal types of current challenges. First, there is the fragmentation with regard to data sets, methods and evaluation metrics, which makes it difficult to compare studies and achieve scientific progress. We addressed this problem by providing authors with two datasets suitable for the evaluation of distributional models, together with the corpora that can be used for their construction. As a result, the performance of very different approaches can be easily compared across papers. Second, these datasets were chosen so as to reflect two of the most pressing issues in the development of distributional models nowadays: differentiation between semantic relations and compositionality. The first set, presented by Baroni and Lenci, includes concrete nouns from different semantic classes (living, non-living, etc.) with associated words for specific semantic relations such as " attribute " , " category coordinate " , " event " , or " metonym ". Panchenko uses this data to compare 21 measures of semantic similarity and relatedness, based on information from WordNet, a traditional corpus, and the web. Baroni, Bruni and Binh Tran explore images as a fourth type of information. Both papers discover fundamental differences in the semantic information that is captured by these different sources of information. This paves the way for a combined, more comprehensive model. The second dataset, borrowed from Mitchell and Lapata, contains phrase similarity judgments. It makes it possible to address the evaluation of distributional models in compositional tasks. Grefenstette and Sadrzadeh show how a transitive verb can be modeled …
Sensation and Perception: An Integrated Approach

Psychophysics The Orienting System The Auditory System Complex Auditory Phenomena I: Psychoacoustics Complex Auditory Phenomena II: Sound as Information Somesthesis I: Kinesthesis and Cutaneous Sense Somesthesis II: Temperature and Pain The Chemical Sensory System I: Taste The Chemical Sensory System II: Smell The Visual System Fundamental Visual Functions and Phenomena The Perception of Color The Perception of Form and Shape The Perception of Movement The Perception of Space I: Spatial Cues and Constancy The Perception of Space II: Illusions The Development of Perception The Development of Perceptual-Motor Coordination The Perception of Time Sensation, Perception, and Attention: Selected Topics Glossary References Author Index Subject Index.
Divorce at Mid and Later Life: Does the Empirical Evidence Support the Theory?.

Little theory-based research has been conduted in the area of mid and later-life divorce depsite the fact that approximatley one in every five divorces falls into that category. This paper reviews the mid-and later-life divorce literature in the context of two currently popular theories of divorce is found. In the discussion, particular reference is given to the need for more rigorous empirical testing of mid- and later-life divorce models.
Enzyme immunoassay for detection of Giardia lamblia cyst antigens in formalin-fixed and unfixed human stool

An antigen-capture enzyme-linked immunosorbent assay employing rabbit and mouse antisera to Giardia lamblia cyst antigens was developed for the diagnosis of Giardia infection through detection of G. lamblia-specific stool antigens in cell-free aqueous eluates of human stool. This is the first report of the use of anti-cyst antibodies in an enzyme immunoassay for G. lamblia. The assay gave a positive result with 54 of 59 stools from patients with symptomatic, clinically diagnosed giardiasis, giving the test a sensitivity of 91.5%. A negative reading was obtained with all of 25 stools from G. lamblia-negative control patients. The assay could detect as few as 20 sonicated cysts added to control stool eluate. The assay was more sensitive to cyst-derived antigens than to trophozoite-derived antigens. With two exceptions, the assay gave a negative result with stools from patients infected with Entamoeba histolytica (seven), Cryptosporidium sp. (four), or Blastocystis hominis (seven) and thus appears to be specific for G. lamblia antigens. Storage of stool eluates for more than 6 months at 4 degrees C as unpreserved aqueous eluates or as formalinized eluates did not affect the ability of the assay to detect the giardial antigens. The enzyme-linked immunosorbent assay proved useful for monitoring the levels of G. lamblia-specific stool antigens in the stool of patients undergoing antigiardial chemotherapy.
Practical data acquisition method for human brain tumor amide proton transfer (APT) imaging

Amide proton transfer (APT) imaging is a type of chemical exchange–dependent saturation transfer (CEST) magnetic resonance imaging (MRI) in which amide protons of endogenous mobile proteins and peptides in tissue are detected. Initial studies have shown promising results for distinguishing tumor from surrounding brain in patients, but these data were hampered by magnetic field inhomogeneity and a low signal‐to‐noise ratio (SNR). Here a practical six‐offset APT data acquisition scheme is presented that, together with a separately acquired CEST spectrum, can provide B0‐inhomogeneity corrected human brain APT images of sufficient SNR within a clinically relevant time frame. Data from nine brain tumor patients at 3T shows that APT intensities were significantly higher in the tumor core, as assigned by gadolinium‐enhancement, than in contralateral normal‐appearing white matter (CNAWM) in patients with high‐grade tumors. Conversely, APT intensities in tumor were indistinguishable from CNAWM in patients with low‐grade tumors. In high‐grade tumors, regions of increased APT extended outside of the core into peripheral zones, indicating the potential of this technique for more accurate delineation of the heterogeneous areas of brain cancers. Magn Reson Med 60:842–849, 2008. © 2008 Wiley‐Liss, Inc.
Marine gravity anomaly from Geosat and ERS 1 satellite altimetry

Closely spaced satellite altimeter profiles collected during the Geosat Geodetic Mission (-6 km) and the ERS 1 Geodetic Phase (8 km) are easily converted to grids of vertical gravity gradient and gravity anomaly. The long-wavelength radial orbit error is suppressed below the noise level of the altimeter by taking the along-track derivative of each profile. Ascending and descending slope profiles are then interpolated onto separate uniform grids. These four grids are combined to form comparable grids of east and north vertical deflection using an iteration scheme that interpolates data gaps with minimum curvature. The vertical gravity gradient is calculated directly from the derivatives of the vertical deflection grids, while Fourier analysis is required to construct gravity anomalies from the two vertical deflection grids. These techniques are applied to a combination of high-density data from the dense mapping phases of Geosat and ERS 1 along with lower-density but higher-accuracy profiles from their repeat orbit phases. A comparison with shipboard gravity data shows the accuracy of the satellite- derived gravity anomaly is about 4-7 mGal for random ship tracks. The accuracy improves to 3 mGal when the ship track follows a Geosat Exact Repeat Mission track line. These data provide the first view of the ocean floor structures in many remote areas of the Earth. Some applications include inertial navigation, prediction of seafloor depth, planning shipboard surveys, plate tectonics, isostasy of volcanoes and spreading ridges, and petroleum exploration.
Evidence for Gradient Salience: What Happens with Competing Non-salient Referents during Pronoun Resolution?

The necessity of a gradient approach to salience ranking of referents introduced in a discourse is evaluated by looking at (unbound) pronoun resolution preferences when there are competing non-salient referents. The study uses a sentencecompletion technique in which participants had to resolve pronouns (“John sprayed the paint on the wall and then it .”). Results suggest that a gradient salience model is necessary. Syntactic and semantic prominence effects on pronoun resolution were also compared with results showing that semantic prominence (i.e., agent > patient) determined the salience ranking of competing referents.
Spectrogram image encoding based on dynamic Hilbert curve routing

In this paper we propose an image-based biological classification system that can identify different creatures via their sounds. The overall system involves the relative spectral transform-perceptual linear prediction for spectrogram image extraction, cosine similarity measure for feature matching, dynamic Hilbert curve for spectrogram routing, and Gaussian mixture model for 1-D spectrogram classification. As an example of our approach, results for honk, dolphin, and whale classification are presented. This method works well on a wide variety of bio-sounds, especially for the highly self-repeated ones. Applications of this approach include biological signal analysis and spectrogram library establishment.
Challenges confronting teachers of English language learners

The number of English language learners and limited English proficient students has grown exponentially in the United States over the past decades. Given the huge cultural and linguistic diversity among them, educating this population of students remains a challenge for teachers. This paper aims to review the types of challenges that educators face when teaching limited English proficient students in the US context. Findings from existing literature show the obstacles teachers confront are social, institutional, and personal in nature. Although some research has emphasised stronger teacher education programmes as a solution to problems related to the teaching and learning of these students, these programmes are insufficient for teachers to overcome all of the challenges they face. Concerted efforts by educators, local and central administrators, academics, local communities, and lawmakers are necessary.
Null controllability of discrete-time linear systems with input and state constraints

This paper presents necessary and sufficient conditions for null controllability of discrete-time linear systems subject to both input and state constraints. The classical results for linear systems without constraints by Kalman and Hautus and for linear systems with only input constraints by Evans, Nguyen and Sontag can be obtained from our main result as particular cases.
SRS: Solving c-Approximate Nearest Neighbor Queries in High Dimensional Euclidean Space with a Tiny Index

Nearest neighbor searches in high-dimensional space have many important applications in domains such as data mining, and multimedia databases. The problem is challenging due to the phenomenon called "curse of dimensionality". An alternative solution is to consider algorithms that returns a c-approximate nearest neighbor (c-ANN) with guaranteed probabilities. Locality Sensitive Hashing (LSH) is among the most widely adopted method, and it achieves high efficiency both in theory and practice. However, it is known to require an extremely high amount of space for indexing, hence limiting its scalability. In this paper, we propose several surprisingly simple methods to answer c-ANN queries with theoretical guarantees requiring only a single tiny index. Our methods are highly flexible and support a variety of functionalities, such as finding the exact nearest neighbor with any given probability. In the experiment, our methods demonstrate superior performance against the state-of-the-art LSH-based methods, and scale up well to 1 billion high-dimensional points on a single commodity PC.
Taylor dispersion of gyrotactic swimming micro-organisms in a linear flow

The theory of generalized Taylor dispersion for suspensions of Brownian particles is developed to study the dispersion of gyrotactic swimming micro-organisms in a linear shear flow. Such creatures are bottom-heavy and experience a gravitational torque which acts to right them when they are tipped away from the vertical. They also suffer a net viscous torque in the presence of a local vorticity field. The orientation of the cells is intrinsically random but the balance of the two torques results in a bias toward a preferred swimming direction. The micro-organisms are sufficiently large that Brownian motion is negligible but their random swimming across streamlines results in a mean velocity together with diffusion. As an example, we consider the case of vertical shear flow and calculate the diffusion coefficients for a suspension of the alga Chlamydomonas nivalis. This rational derivation is compared with earlier approximations for the diffusivity.
Multi-Agent Team Formation: Diversity Beats Strength?

Team formation is a critical step in deploying a multi-agent team. In some scenarios, agents coordinate by voting continuously. When forming such teams, should we focus on the diversity of the team or on the strength of each member? Can a team of diverse (and weak) agents outperform a uniform team of strong agents? We propose a new model to address these questions. Our key contributions include: (i) we show that a diverse team can overcome a uniform team and we give the necessary conditions for it to happen; (ii) we present optimal voting rules for a diverse team; (iii) we perform synthetic experiments that demonstrate that both diversity and strength contribute to the performance of a team; (iv) we show experiments that demonstrate the usefulness of our model in one of the most difficult challenges for Artificial Intelligence: Computer Go.
Shape of the association between income and mortality: a cohort study of Denmark, Finland, Norway and Sweden in 1995 and 2003

Objectives Prior work has examined the shape of the income–mortality association, but work has not compared gradients between countries. In this study, we focus on changes over time in the shape of income–mortality gradients for 4 Nordic countries during a period of rising income inequality. Context and time differentials in shape imply that the relationship between income and mortality is not fixed. Setting Population-based cohort study of Denmark, Finland, Norway and Sweden. Participants We collected data on individuals aged 25 or more in 1995 (n=12.98 million individuals, 0.84 million deaths) and 2003 (n=13.08 million individuals, 0.90 million deaths). We then examined the household size equivalised disposable income at the baseline year in relation to the rate of mortality in the following 5 years. Results A steep income gradient in mortality in men and women across all age groups except the oldest old in Denmark, Finland, Norway and Sweden. From the 1990s to 2000s mortality dropped, but generally more so in the upper part of the income distribution than in the lower part. As a consequence, the shape of the income gradient in mortality changed. The shift in the shape of the association was similar in all 4 countries. Conclusions A non-linear gradient exists between income and mortality in most cases and because of a more rapid mortality decline among those with high income the income gradient has become steeper over time.
Intra-Amniotic Infection/Inflammation as a Risk Factor for Subsequent Ruptured Membranes after Clinically Indicated Amniocentesis in Preterm Labor

The aim of this study was to determine whether intra-amniotic infection/inflammation (IAI) was associated with subsequent ruptured membranes in women with preterm labor and intact membranes who had a clinically indicated amniocentesis. This retrospective cohort study included 237 consecutive women with preterm labor (20-34.6 weeks) who underwent amniocentesis. The clinical and laboratory parameters evaluated included demographic variables, gestational age, C-reactive protein (CRP) and amniotic fluid (AF) white blood cell, interleukin-6 (IL-6) and culture results. IAI was defined as a positive AF culture and/or an elevated AF IL-6 level (>2.6 ng/mL). The primary outcome was ruptured membranes in the absence of active labor occurring within 48 hours of amniocentesis. Preterm premature rupture of membranes subsequently developed in 10 (4.2%) women within 48 hr of amniocentesis. Multivariate analysis demonstrated that only IAI was independently associated with the ruptured membranes occurring within 48 hr of amniocentesis. In the predictive model based on variables assessed before amniocentesis, only CRP level was retained. IAI is an independent risk factor for subsequent ruptured membranes after clinically indicated amniocentesis in preterm labor. Prior to amniocentesis, measurement of serum CRP level can provide a risk assessment for the subsequent development of ruptured membranes after the procedure.
The Windy Domain - A Challenging Real-World Application of Integrated Planning and Scheduling

Many renewable sources of energy can harness greater uptime and power output when located in remote and potentially hostile locations. One example of this is wind power, wherein turbines positioned at offshore locations can experience higher and more sustained windspeeds than their onshore counterparts. However, these traits also lead to increased load and degradation upon components, which in turn means that regular maintenance is required. While onshore maintenance costs are relatively trivial, the costs associated with offshore maintenance can be several orders-of-magnitude greater.Traditionally, the scheduling of these repairs is performed by hand using a set of pre-determined plans for specific fault-categories (e.g. trivial/minor/major component replacement). This paper formulates this problem as a PDDL domain which encapsulates all of the individual pre-defined plans in a single representation, such that multiple levels of response can be integrated in a single plan. The domain presented is complex in that it contains not only numeric and temporal planning aspects, but that a subset of the domain is heavily geared towards pure scheduling. We include performance results on how a state-of-the-art planner performs on various example scenarios.
Computational Quantification of Peptides from LC-MS Data

Liquid chromatography coupled to mass spectrometry (LC-MS) has become a major tool for the study of biological processes. High-throughput LC-MS experiments are frequently conducted in modern laboratories, generating an enormous amount of data per day. A manual inspection is therefore no longer a feasible task. Consequently, there is a need for computational tools that can rapidly provide information about mass, elution time, and abundance of the compounds in a LC-MS sample. We present an algorithm for the detection and quantification of peptides in LC-MS data. Our approach is flexible and independent of the MS technology in use. It is based on a combination of the sweep line paradigm with a novel wavelet function tailored to detect isotopic patterns of peptides. We propose a simple voting schema to use the redundant information in consecutive scans for an accurate determination of monoisotopic masses and charge states. By explicitly modeling the instrument inaccuracy, we are also able to cope with data sets of different quality and resolution. We evaluate our technique on data from different instruments and show that we can rapidly estimate mass, centroid of retention time, and abundance of peptides in a sound algorithmic framework. Finally, we compare the performance of our method to several other techniques on three data sets of varying complexity.
Understanding the role of retail store service in light of self-image-store image congruence

The relationships among a consumer’s service experience, the servicescape, and perceived value for the money spent on the service are important theoretical as well as practical issues. Little effort has, however, been devoted to understanding these in the context of the effects of consumer image–store image congruency. Image congruency has not only been shown to be valuable in relation to product choices, but has also been shown to contribute to our understanding of retail store choice and preferences. This study examines the effect of retailer service provision and the retail store environment (servicescape) on the customers’ perception of value for money. The study also examines the role of self–store image congruence in the above relationships. The findings confirm the hypothesized relationships in the conceptual model (except for servicescape effects). The findings also indicate that the effects are stronger for those individuals experiencing high self–store image congruence.