ACE Dataset Research Articles

WarpEngine, a Flexible Platform for Distributed Computing Implemented in the VEGA Program and Specially Targeted for Virtual Screening Studies.

The manuscript describes WarpEngine, a novel platform implemented within the VEGA ZZ suite of software for performing distributed simulations both in local and wide area networks. Despite being tailored for structure-based virtual screening campaigns, WarpEngine possesses the required flexibility to carry out distributed calculations utilizing various pieces of software, which can be easily encapsulated within this platform without changing their source codes. WarpEngine takes advantages of all cheminformatics features implemented in the VEGA ZZ program as well as of its largely customizable scripting architecture thus allowing an efficient distribution of various time-demanding simulations. To offer an example of the WarpEngine potentials, the manuscript includes a set of virtual screening campaigns based on the ACE data set of the DUD-E collections using PLANTS as the docking application. Benchmarking analyses revealed a satisfactory linearity of the WarpEngine performances, the speed-up values being roughly equal to the number of utilized cores. Again, the computed scalability values emphasized that a vast majority (i.e., >90%) of the performed simulations benefit from the distributed platform presented here. WarpEngine can be freely downloaded along with the VEGA ZZ program at www.vegazz.net .

V-JAUNE

Video summarization and action recognition are two important areas of multimedia video analysis. While these two areas have been tackled separately to date, in this article, we present a latent structural SVM framework to recognize the action and derive the summary of a video in a joint, simultaneous fashion. Efficient inference is provided by a submodular score function that accounts for the action and summary jointly. In this article, we also define a novel measure to evaluate the quality of a predicted video summary against the annotations of multiple annotators. Quantitative and qualitative results over two challenging action datasets—the ACE and MSR DailyActivity3D datasets—show that the proposed joint approach leads to higher action recognition accuracy and equivalent or better summary quality than comparable approaches that perform these tasks separately.

Ensemble Space-Time Correlation of Plasma Turbulence in the Solar Wind.

Single point measurement turbulence cannot distinguish variations in space and time. We employ an ensemble of one- and two-point measurements in the solar wind to estimate the space-time correlation function in the comoving plasma frame. The method is illustrated using near Earth spacecraft observations, employing ACE, Geotail, IMP-8, and Wind data sets. New results include an evaluation of both correlation time and correlation length from a single method, and a new assessment of the accuracy of the familiar frozen-in flow approximation. This novel view of the space-time structure of turbulence may prove essential in exploratory space missions such as Solar Probe Plus and Solar Orbiter for which the frozen-in flow hypothesis may not be a useful approximation.

A Probabilistic Soft Logic Based Approach to Exploiting Latent and Global Information in Event Classification

Global information such as event-event association, and latent local information such as fine-grained entity types, are crucial to event classification. However, existing methods typically focus on sophisticated local features such as part-of-speech tags, either fully or partially ignoring the aforementioned information. By contrast, this paper focuses on fully employing them for event classification. We notice that it is difficult to encode some global information such as event-event association for previous methods. To resolve this problem, we propose a feasible approach which encodes global information in the form of logic using Probabilistic Soft Logic model. Experimental results show that, our proposed approach advances state-of-the-art methods, and achieves the best F1 score to date on the ACE data set.

Characterization of Architectural Distortion in Mammograms Based on Texture Analysis Using Support Vector Machine Classifier with Clinical Evaluation.

Architecture distortion (AD) is an important and early sign of breast cancer, but due to its subtlety, it is often missed on the screening mammograms. The objective of this study is to create a quantitative approach for texture classification of AD based on various texture models, using support vector machine (SVM) classifier. The texture analysis has been done on the region of interest (ROI) selected from the original mammogram. A comprehensive analysis has been done on samples from three databases; out of which, two data sets are from the public domain, and the third data set is for clinical evaluation. The public domain databases are IRMA version of digital database for screening mammogram (DDSM) and Mammographic Image Analysis Society (MIAS). For clinical evaluation, the actual patient's database has been obtained from ACE Healthways, Diagnostic Centre Ludhiana, India. The significant finding of proposed study lies in appropriate selection of the size of ROIs. The experiments have been done on fixed size of ROIs as well as on the ground truth (variable size) ROIs. Best results pertain to an accuracy of 92.94% obtained in case of DDSM database for fixed-size ROIs. In case of MIAS database, an accuracy of 95.34% is achieved in AD versus non-AD (normal) cases for ground truth ROIs. Clinically, an accuracy of 88% was achieved for ACE dataset. The results obtained in the present study are encouraging, as optimal result has been achieved for the proposed study in comparison with other related work in the same area.

A new four‐plasma categorization scheme for the solar wind

AbstractA three‐parameter algebraic scheme is developed to categorize the solar wind at 1 AU into four plasma types: coronal‐hole‐origin plasma, streamer‐belt‐origin plasma, sector‐reversal‐region plasma, and ejecta. The three parameters are the proton‐specific entropy Sp = Tp/np2/3, the proton Alfvén speed vA, and the proton temperature Tp compared with a velocity‐dependent expected temperature. Four measurements are needed to apply the scheme: the proton number density np, the proton temperature Tp, the magnetic field strength B, and the solar wind speed vsw. The scheme is tested and is found to be more accurate than existing categorization schemes. The categorization scheme is applied to the 1963–2013 OMNI2 data set spanning four solar cycles and to the 1998–2008 ACE data set. The statistical properties of the four types of plasma are examined. The sector‐reversal‐region plasma is found to have statistically low alpha‐to‐proton density ratios and high Alfvén Mach numbers. The statistical relations between the proton and alpha‐particle‐specific entropies and oxygen and carbon charge‐state‐density ratios Sp, Sα, O7+/O6+, and C6+/C5+ from ACE are examined for the four types of plasma: the patterns observed imply a connection between sector‐reversal‐region plasma and ejecta and a connection between streamer‐belt‐origin plasma and coronal‐hole‐origin plasma. Plasma occurrence rates are examined and solar cycle patterns are found for ejecta, for coronal‐hole‐origin plasma, and for sector‐reversal‐region plasma.

Follow-up of loci from the International Genomics of Alzheimer’s Disease Project identifies TRIP4 as a novel susceptibility gene

To follow-up loci discovered by the International Genomics of Alzheimer's Disease Project, we attempted independent replication of 19 single nucleotide polymorphisms (SNPs) in a large Spanish sample (Fundació ACE data set; 1808 patients and 2564 controls). Our results corroborate association with four SNPs located in the genes INPP5D, MEF2C, ZCWPW1 and FERMT2, respectively. Of these, ZCWPW1 was the only SNP to withstand correction for multiple testing (P=0.000655). Furthermore, we identify TRIP4 (rs74615166) as a novel genome-wide significant locus for Alzheimer's disease risk (odds ratio=1.31; confidence interval 95% (1.19–1.44); P=9.74 × 10−9).

ATP5H/KCTD2 locus is associated with Alzheimer's disease risk

To identify loci associated with Alzheimer disease, we conducted a three-stage analysis using existing genome-wide association studies (GWAS) and genotyping in a new sample. In Stage I, all suggestive single-nucleotide polymorphisms (at P<0.001) in a previously reported GWAS of seven independent studies (8082 Alzheimer's disease (AD) cases; 12 040 controls) were selected, and in Stage II these were examined in an in silico analysis within the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium GWAS (1367 cases and 12904 controls). Six novel signals reaching P<5 × 10−6 were genotyped in an independent Stage III sample (the Fundació ACE data set) of 2200 sporadic AD patients and 2301 controls. We identified a novel association with AD in the adenosine triphosphate (ATP) synthase, H+ transporting, mitochondrial F0 (ATP5H)/Potassium channel tetramerization domain-containing protein 2 (KCTD2) locus, which reached genome-wide significance in the combined discovery and genotyping sample (rs11870474, odds ratio (OR)=1.58, P=2.6 × 10−7 in discovery and OR=1.43, P=0.004 in Fundació ACE data set; combined OR=1.53, P=4.7 × 10−9). This ATP5H/KCTD2 locus has an important function in mitochondrial energy production and neuronal hyperpolarization during cellular stress conditions, such as hypoxia or glucose deprivation.

Narrowing the Modeling Gap: A Cluster-Ranking Approach to Coreference Resolution

Traditional learning-based coreference resolvers operate by training the mention-pair model for determining whether two mentions are coreferent or not. Though conceptually simple and easy to understand, the mention-pair model is linguistically rather unappealing and lags far behind the heuristic-based coreference models proposed in the pre-statistical NLP era in terms of sophistication. Two independent lines of recent research have attempted to improve the mention-pair model, one by acquiring the mention-ranking model to rank preceding mentions for a given anaphor, and the other by training the entity-mention model to determine whether a preceding cluster is coreferent with a given mention. We propose a cluster-ranking approach to coreference resolution, which combines the strengths of the mention-ranking model and the entity-mention model, and is therefore theoretically more appealing than both of these models. In addition, we seek to improve cluster rankers via two extensions: (1) lexicalization and (2) incorporating knowledge of anaphoricity by jointly modeling anaphoricity determination and coreference resolution. Experimental results on the ACE data sets demonstrate the superior performance of cluster rankers to competing approaches as well as the effectiveness of our two extensions.

Magnetic Discontinuities in the Near-Earth Solar Wind: Evidence of In-Transit Turbulence or Remnants of Coronal Structure?

Fluctuations in the solar wind plasma and magnetic field are well described by the sum of two power law distributions. It has been postulated that these distributions are the result of two independent processes: turbulence, which contributes mainly to the smaller fluctuations, and crossing the boundaries of flux tubes of coronal origin, which dominates the larger variations. In this study we explore the correspondence between changes in the magnetic field with changes in other solar wind properties. Changes in density and temperature may result from either turbulence or coronal structures, whereas changes in composition, such as the alpha-to-proton ratio are unlikely to arise from in-transit effects. Observations spanning the entire ACE dataset are compared with a null hypothesis of no correlation between magnetic field discontinuities and changes in other solar wind parameters. Evidence for coronal structuring is weaker than for in-transit turbulence, with only ∼ 25% of large magnetic field discontinuities associated with a significant change in the alpha-to-proton ratio, compared to ∼ 40% for significant density and temperature changes. However, note that a lack of detectable alpha-to-proton signature is not sufficient to discount a structure as having a solar origin.

Unsupervised Coreference Resolution with HyperGraph Partitioning

Unsupervised-learning based coreference resolution obviates the need for annotation of training data. However, unsupervised approaches have traditionally been relying on the use of mention-pair models, which only consider information pertaining to a pair of mentions at a time. In this paper, it is proposed the use of hypergraph partitioning to overcome this limitation. The mentions are modeled as vertices. By allowing a hyperedge to cover multiple mentions that share a common property, the additional information beyond a mention pair can be captured. This paper introduces a hypergraph partitioning algorithm that divides mentions directly into equivalence classes representing individual entities. Evaluation on the ACE dataset shows that our unsupervised hypergraph based approach outperforms previous unsupervised methods.

Reconstruction of a large-scale reconnection exhaust structure in the solar wind

Abstract. We recover two-dimensional (2-D) magnetic field and flow field configurations from three spacecraft encounters with a single large-scale reconnection exhaust structure in the solar wind, using a new reconstruction method (Sonnerup and Teh, 2008) based on the ideal single-fluid MHD equations in a steady-state, 2-D geometry. The reconstruction is performed in the rest frame of the X-line, where the flow into, and the plasma jetting within, the exhaust region are clearly visible. The event was first identified by Phan et al. (2006) in the ACE, Cluster, and Wind data sets; they argued that quasi-steady reconnection persisted for over 2 h at a long (390 RE) X-line. The reconnection exhaust is sandwiched between two discontinuities, both of which contain elements of intermediate- and slow-mode behavior; these elements are co-located rather than being spatially separated. These composite discontinuities do not satisfy the coplanarity condition or the standard MHD jump conditions. For all three spacecraft, the Walén regression line slope was positive (negative) for the leading (trailing) discontinuity. Our MHD reconstruction shows that: (1) the X-line orientation was close to the bisector of the overall magnetic shear angle and exhibited a slow rotating motion toward the Sun-Earth line; (2) the X-line moved earthward, dawnward, and southward; (3) the reconnection electric field was small (~0.02 mV/m on average) and gradually decreased from the first crossing (ACE) to the last (Wind). The magnetic field and flow field configurations recovered from ACE and Cluster are similar while those recovered from Wind also include a magnetic island and an associated vortex. Reconnection persisted for at least 2.4 h involving inflow into the exhaust region from its two sides. Time-dependence in the reconnection electric fields seen by ACE and Wind indicates local temporal variations in the field configuration. In addition to the reconstruction results, we provide a description and analysis of many details from the crossings by the spacecraft.

Linguistic Theory Based Contextual Evidence Mining for Statistical Chinese Co-Reference Resolution

Under statistical learning framework, the paper focuses on how to use traditional linguistic findings on anaphora resolution as a guide for mining and organizing contextual features for Chinese co-reference resolution. The main achievements are as follows. (1) In order to simulate "syntactic and semantic parallelism factor", we extract "bags of word form and POS" feature and "bag of semes" feature from the contexts of the entity mentions and incorporate them into the baseline feature set. (2) Because it is too coarse to use the feature of bags of word form, POS tag and seme to determine the syntactic and semantic parallelism between two entity mentions, we propose a method for contextual feature reconstruction based on semantic similarity computation, in order that the reconstructed contextual features could better approximate the anaphora resolution factor of "Syntactic and Semantic Parallelism Preferences". (3) We use an entity-mention-based contextual feature representation instead of isolated word-based contextual feature representation, and expand the size of the contextual windows in addition, in order to approximately simulate "the selectional restriction factor" for anaphora resolution. The experiments show that the multi-level contextual features are useful for co-reference resolution, and the statistical system incorporated with these features performs well on the standard ACE datasets.

The effects of multiple features of alternatively spliced exons on the K A /K S ratio test

BackgroundThe evolution of alternatively spliced exons (ASEs) is of primary interest because these exons are suggested to be a major source of functional diversity of proteins. Many exon features have been suggested to affect the evolution of ASEs. However, previous studies have relied on the KA/KS ratio test without taking into consideration information sufficiency (i.e., exon length > 75 bp, cross-species divergence > 5%) of the studied exons, leading to potentially biased interpretations. Furthermore, which exon feature dominates the results of the KA/KS ratio test and whether multiple exon features have additive effects have remained unexplored.ResultsIn this study, we collect two different datasets for analysis – the ASE dataset (which includes lineage-specific ASEs and conserved ASEs) and the ACE dataset (which includes only conserved ASEs). We first show that information sufficiency can significantly affect the interpretation of relationship between exons features and the KA/KS ratio test results. After discarding exons with insufficient information, we use a Boolean method to analyze the relationship between test results and four exon features (namely length, protein domain overlapping, inclusion level, and exonic splicing enhancer (ESE) frequency) for the ASE dataset. We demonstrate that length and protein domain overlapping are dominant factors, and they have similar impacts on test results of ASEs. In addition, despite the weak impacts of inclusion level and ESE motif frequency when considered individually, combination of these two factors still have minor additive effects on test results. However, the ACE dataset shows a slightly different result in that inclusion level has a marginally significant effect on test results. Lineage-specific ASEs may have contributed to the difference. Overall, in both ASEs and ACEs, protein domain overlapping is the most dominant exon feature while ESE frequency is the weakest one in affecting test results.ConclusionThe proposed method can easily find additive effects of individual or multiple factors on the KA/KS ratio test results of exons. Therefore, the system can analyze complex conditions in evolution where multiple features are involved. More factors can also be added into the system to extend the scope of evolutionary analysis of exons. In addition, our method may be useful when orthologous exons can not be found for the KA/KS ratio test.

Comparison of atmospheric retrievals from ACE and HALOE

Atmospheric retrievals from ACE are compared to nearly coincident measurements from HALOE. Comparisons are done for O3, HF, HCl, H2O, CH4, NO, NO2, and temperature. Statistics from 32 profile pairs that were reasonably close in space and time are presented. The temperature retrievals agree to within ±2 K, an important result as all other retrievals depend on this. ACE gas retrievals are typically higher than HALOE, with the exception of NO and NO2 where HALOE is higher. This comparison helps provide traceability between two important complementary remote sensing missions, lends confidence to the initial ACE dataset, and provides a baseline for future retrieval improvements.

THE ACE MAGNETIC FIELDS EXPERIMENT

The magnetic field experiment on ACE provides continuous measurements of the local magnetic field in the interplanetary medium. These measurements are essential in the interpretation of simultaneous ACE observations of energetic and thermal particles distributions. The experiment consists of a pair of twin, boom-mounted, triaxial fluxgate sensors which are located 165 inches (= 4.19 m) from the center of the spacecraft on opposing solar panels. The electronics and digital processing unit (DPU) is mounted on the top deck of the spacecraft. The two triaxial sensors provide a balanced, fully redundant vector instrument and permit some enhanced assessment of the spacecraft’s magnetic field. The instrument provides data for Browse and high-level products with between 3 and 6 vector s-1 resolution for continuous coverage of the interplanetary magnetic field. Two high-resolution snapshot buffers each hold 297 s of 24 vector s-1 data while on-board Fast Fourier Transforms extend the continuous data to 12 Hz resolution. Real-time observations with 1-s resolution are provided continuously to the Space Environmental Center (SEC) of the National Oceanographie and Atmospheric Association (NO A A) for near-instantaneous, world-wide dissemination in service to space weather studies. As has been our team’s tradition, high instrument reliability is obtained by the use of fully redundant systems and extremely conservative designs. We plan studies of the interplanetary medium in support of the fundamental goals of the ACE mission and cooperative studies with other ACE investigators using the combined ACE dataset as well as other ISTP spacecraft involved in the general program of Sun—Earth Connections.