scholarly journals Density-based outlier scoring on Kepler data

2020 ◽  
Vol 499 (1) ◽  
pp. 524-542
Author(s):  
Daniel K Giles ◽  
Lucianne Walkowicz
Keyword(s):  
Large Scale ◽  
Feature Space ◽  
Systematic Errors ◽  
Light Curves ◽  
Nearest Neighbour ◽  
Mining Method ◽  
Discovery Process ◽  
Anomalous Data ◽  
New Challenges ◽  
Object Classes

ABSTRACT In the present era of large-scale surveys, big data present new challenges to the discovery process for anomalous data. Such data can be indicative of systematic errors, extreme (or rare) forms of known phenomena, or most interestingly, truly novel phenomena that exhibit as-of-yet unobserved behaviours. In this work, we present an outlier scoring methodology to identify and characterize the most promising unusual sources to facilitate discoveries of such anomalous data. We have developed a data mining method based on k-nearest neighbour distance in feature space to efficiently identify the most anomalous light curves. We test variations of this method including using principal components of the feature space, removing select features, the effect of the choice of k, and scoring to subset samples. We evaluate the performance of our scoring on known object classes and find that our scoring consistently scores rare (<1000) object classes higher than common classes. We have applied scoring to all long cadence light curves of Quarters 1–17 of Kepler’s prime mission and present outlier scores for all 2.8 million light curves for the roughly 200k objects.

Linguistically Annotated Reordering: Evaluation and Analysis

2010 ◽  
Vol 36 (3) ◽  
pp. 535-568 ◽  
Author(s):  
Deyi Xiong ◽  
Min Zhang ◽  
Aiti Aw ◽  
Haizhou Li
Keyword(s):  
Large Scale ◽  
Statistical Machine Translation ◽  
Training Data ◽  
Linguistic Knowledge ◽  
Analysis Method ◽  
Complementary Information ◽  
New Approach ◽  
Parse Trees ◽  
New Challenges ◽  
Insight Into

Linguistic knowledge plays an important role in phrase movement in statistical machine translation. To efficiently incorporate linguistic knowledge into phrase reordering, we propose a new approach: Linguistically Annotated Reordering (LAR). In LAR, we build hard hierarchical skeletons and inject soft linguistic knowledge from source parse trees to nodes of hard skeletons during translation. The experimental results on large-scale training data show that LAR is comparable to boundary word-based reordering (BWR) (Xiong, Liu, and Lin 2006), which is a very competitive lexicalized reordering approach. When combined with BWR, LAR provides complementary information for phrase reordering, which collectively improves the BLEU score significantly. To further understand the contribution of linguistic knowledge in LAR to phrase reordering, we introduce a syntax-based analysis method to automatically detect constituent movement in both reference and system translations, and summarize syntactic reordering patterns that are captured by reordering models. With the proposed analysis method, we conduct a comparative analysis that not only provides the insight into how linguistic knowledge affects phrase movement but also reveals new challenges in phrase reordering.

scholarly journals Magnetic field transport in compact binaries

2020 ◽  
Vol 641 ◽  
pp. A133
Author(s):  
N. Scepi ◽  
G. Lesur ◽  
G. Dubus ◽  
J. Jacquemin-Ide
Keyword(s):  
Magnetic Field ◽  
Angular Momentum ◽  
Magnetic Flux ◽  
Large Scale ◽  
Compact Object ◽  
Light Curves ◽  
Momentum Transport ◽  
Local Magnetization ◽  
Angular Momentum Transport ◽  
The Impact

Context. Dwarf novæ (DNe) and low mass X-ray binaries (LMXBs) show eruptions that are thought to be due to a thermal-viscous instability in their accretion disk. These eruptions provide constraints on angular momentum transport mechanisms. Aims. We explore the idea that angular momentum transport could be controlled by the dynamical evolution of the large-scale magnetic field. We study the impact of different prescriptions for the magnetic field evolution on the dynamics of the disk. This is a first step in confronting the theory of magnetic field transport with observations. Methods. We developed a version of the disk instability model that evolves the density, the temperature, and the large-scale vertical magnetic flux simultaneously. We took into account the accretion driven by turbulence or by a magnetized outflow with prescriptions taken, respectively, from shearing box simulations or self-similar solutions of magnetized outflows. To evolve the magnetic flux, we used a toy model with physically motivated prescriptions that depend mainly on the local magnetization β, where β is the ratio of thermal pressure to magnetic pressure. Results. We find that allowing magnetic flux to be advected inwards provides the best agreement with DNe light curves. This leads to a hybrid configuration with an inner magnetized disk, driven by angular momentum losses to an MHD outflow, sharply transiting to an outer weakly-magnetized turbulent disk where the eruptions are triggered. The dynamical impact is equivalent to truncating a viscous disk so that it does not extend down to the compact object, with the truncation radius dependent on the magnetic flux and evolving as Ṁ−2/3. Conclusions. Models of DNe and LMXB light curves typically require the outer, viscous disk to be truncated in order to match the observations. There is no generic explanation for this truncation. We propose that it is a natural outcome of the presence of large-scale magnetic fields in both DNe and LMXBs, with the magnetic flux accumulating towards the center to produce a magnetized disk with a fast accretion timescale.

scholarly journals EpiScanpy: integrated single-cell epigenomic analysis

2019 ◽  
Author(s):  
Anna Danese ◽  
Maria L. Richter ◽  
David S. Fischer ◽  
Fabian J. Theis ◽  
Maria Colomé-Tatché
Keyword(s):  
Dna Methylation ◽  
Single Cell ◽  
Large Scale ◽  
Feature Space ◽  
Rna Seq ◽  
Computational Framework ◽  
Learning Techniques ◽  
Multiple Feature ◽  
The Many ◽  
Cell Data

ABSTRACTEpigenetic single-cell measurements reveal a layer of regulatory information not accessible to single-cell transcriptomics, however single-cell-omics analysis tools mainly focus on gene expression data. To address this issue, we present epiScanpy, a computational framework for the analysis of single-cell DNA methylation and single-cell ATAC-seq data. EpiScanpy makes the many existing RNA-seq workflows from scanpy available to large-scale single-cell data from other -omics modalities. We introduce and compare multiple feature space constructions for epigenetic data and show the feasibility of common clustering, dimension reduction and trajectory learning techniques. We benchmark epiScanpy by interrogating different single-cell brain mouse atlases of DNA methylation, ATAC-seq and transcriptomics. We find that differentially methylated and differentially open markers between cell clusters enrich transcriptome-based cell type labels by orthogonal epigenetic information.

scholarly journals The Differential Astrometric Reference Frame on short timescales in the Gaia Era

2017 ◽  
Vol 12 (S330) ◽  
pp. 79-80
Author(s):  
Ummi Abbas ◽  
Beatrice Bucciarelli ◽  
Mario G. Lattanzi ◽  
Mariateresa Crosta ◽  
Mario Gai ◽  
...  
Keyword(s):  
Reference Frame ◽  
Large Scale ◽  
Total Error ◽  
Systematic Errors ◽  
Field Of View ◽  
Small Field ◽  
Inertial Reference Frame ◽  
Error Budget ◽  
G 14 ◽  
High Cadence

AbstractWe use methods of differential astrometry to construct a small field inertial reference frame stable at the micro-arcsecond level. Using Gaia measurements of field angles we look at the influence of the number of reference stars and the stars magnitude as well as astrometric systematics on the total error budget with the help of Gaia-like simulations around the Ecliptic Pole in a differential astrometric scenario. We find that the systematic errors are modeled and reliably estimated to the μas level even in fields with a modest number of 37 stars with G <13 mag over a 0.24 sq. degrees field of view for short timescales of the order of a day for a perfect instrument and with high-cadence observations. Accounting for large-scale calibrations by including the geometric instrument model over such short timescales requires fainter stars down to G=14 mag without diminishing the accuracy of the reference frame.

Experience of CAE Fidesys application in numerical geomechanical modelling of Zhdanovskoe deposit

2022 ◽  
pp. 94-98
Author(s):  
L.K. Miroshnikova ◽  
A.Yu. Mezentsev ◽  
G.A. Kadyralieva ◽  
M.A. Perepelkin
Keyword(s):  
Rock Mass ◽  
Large Scale ◽  
Strain State ◽  
Raw Material ◽  
Mining Method ◽  
Stress Strain ◽  
Stress Strain State ◽  
Ore Bodies ◽  
Sublevel Caving ◽  
Murmansk Region

The Zhdanovskoe copper-nickel sulfide ores deposit is located in the north-west of the Murmansk region and is a mineral raw material source for JSC «Kola MMC». The main mining method used is sublevel caving. In some areas, due to the complex shape of the ore bodies, the open stoping mining method is used which requires determining stable parameters of stopes and pillars. It is necessary to study the stress-strain state of the deposit to ensure safe mining conditions. One of the possible solutions is the modeling of the stress-strain state of rock mass using the finite element method, for example, CAE Fidesys, which is FEMbased software. The use of CAE Fidesys for solving geomechanics tasks allows creating models of individual excavation units to determine the stability of stopes and pillars, and large-scale models that include several ore bodies and areas of the host rock mass. The article considers solutions of both types of geomechanic tasks using CAE Fidesys for conditions of the Zhdanovskoe deposit.

Deep-Ocean Exploitation Technology

1971 ◽  
Vol 8 (02) ◽  
pp. 145-158
Author(s):  
Raymond Kaufman
Keyword(s):  
Continental Shelf ◽  
Large Scale ◽  
Deep Ocean ◽  
Abandoned Mine ◽  
Economic Potential ◽  
Mining Method ◽  
Mine Shaft ◽  
Sea Floor ◽  
Ocean Mining ◽  
Air Lift

The paper discusses the latest techniques proposed for mining minerals from the deep ocean. Deep ocean is defined as the sea beyond the continental shelf, particularly areas of the sea floor exceeding 1200 ft in depth. The three principal deep-ocean minerals having economic potential in the immediate future are identified. Four recently proposed advanced deep-ocean mining concepts are presented. Use of the air-lift pump as a viable mining method is discussed and a large-scale air-lift pump experiment conducted in an abandoned mine shaft at Galax, Virginia is described. The principal features of the conversion of a small C1-M-AV1 type cargo ship to a deep-ocean mining prototype vessel, RV Deepsea Miner, is outlined.

DeepEnzyPred: A Bi-Layered Deep Learning Framework for prediction of Bacteriophage Enzymes and their Sub-Hydrolases Enzymes via Novel Multi Level- Multi Thresholds Feature Selection technique

2020 ◽  
Author(s):  
Yu Wang ◽  
ZAHEER ULLAH KHAN ◽  
Shaukat Ali ◽  
Maqsood Hayat
Keyword(s):  
Feature Selection ◽  
Bacterial Infections ◽  
Large Scale ◽  
Computational Models ◽  
Genetic Material ◽  
Feature Space ◽  
Classification Model ◽  
Antibacterial Drug ◽  
Accurate Identification ◽  
Multi Level

Abstract BackgroundBacteriophage or phage is a type of virus that replicates itself inside bacteria. It consist of genetic material surrounded by a protein structure. Bacteriophage plays a vital role in the domain of phage therapy and genetic engineering. Phage and hydrolases enzyme proteins have a significant impact on the cure of pathogenic bacterial infections and disease treatment. Accurate identification of bacteriophage proteins is important in the host subcellular localization for further understanding of the interaction between phage, hydrolases, and in designing antibacterial drugs. Looking at the significance of Bacteriophage proteins, besides wet laboratory-based methods several computational models have been developed so far. However, the performance was not considerable due to inefficient feature schemes, redundancy, noise, and lack of an intelligent learning engine. Therefore we have developed an anovative bi-layered model name DeepEnzyPred. A Hybrid feature vector was obtained via a novel Multi-Level Multi-Threshold subset feature selection (MLMT-SFS) algorithm. A two-dimensional convolutional neural network was adopted as a baseline classifier.ResultsA conductive hybrid feature was obtained via a serial combination of CTD and KSAACGP features. The optimum feature was selected via a Novel Multi-Level Multi-Threshold Subset Feature selection algorithm. Over 5-fold jackknife cross-validation, an accuracy of 91.6 %, Sensitivity of 63.39%, Specificity 95.72%, MCC of 0.6049, and ROC value of 0.8772 over Layer-1 were recorded respectively. Similarly, the underline model obtained an Accuracy of 96.05%, Sensitivity of 96.22%, Specificity of 95.91%, MCC of 0.9219, and ROC value of 0.9899 over layer-2 respectivily.ConclusionThis paper presents a robust and effective classification model was developed for bacteriophage and their types. Primitive features were extracted via CTD and KSAACGP. A novel method (MLMT-SFS ) was devised for yielding optimum hybrid feature space out of primitive features. The result drew over hybrid feature space and 2D-CNN shown an excellent classification. Based on the recorded results, we believe that the developed predictor will be a valuable resource for large scale discrimination of unknown Phage and hydrolase enzymes in particular and new antibacterial drug design in pharmaceutical companies in general.

scholarly journals X-ray light curves from realistic polar cap models: inclined pulsar magnetospheres and multipole fields

2019 ◽  
Vol 490 (2) ◽  
pp. 1774-1783 ◽  
Author(s):  
Will Lockhart ◽  
Samuel E Gralla ◽  
Feryal Özel ◽  
Dimitrios Psaltis
Keyword(s):  
Light Curve ◽  
Quadrupole Moment ◽  
Large Scale ◽  
Realistic Model ◽  
Light Curves ◽  
X Ray ◽  
Polar Caps ◽  
Thin Ring ◽  
Magnetic Quadrupole ◽  
South Asymmetry

ABSTRACT Thermal X-ray emission from rotation-powered pulsars is believed to originate from localized ‘hotspots’ on the stellar surface occurring where large-scale currents from the magnetosphere return to heat the atmosphere. Light-curve modelling has primarily been limited to simple models, such as circular antipodal emitting regions with constant temperature. We calculate more realistic temperature distributions within the polar caps, taking advantage of recent advances in magnetospheric theory, and we consider their effect on the predicted light curves. The emitting regions are non-circular even for a pure dipole magnetic field, and the inclusion of an aligned magnetic quadrupole moment introduces a north–south asymmetry. As the quadrupole moment is increased, one hotspot grows in size before becoming a thin ring surrounding the star. For the pure dipole case, moving to the more realistic model changes the light curves by $5\!-\!10{{\, \rm per\, cent}}$ for millisecond pulsars, helping to quantify the systematic uncertainty present in current dipolar models. Including the quadrupole gives considerable freedom in generating more complex light curves. We explore whether these simple dipole+quadrupole models can account for the qualitative features of the light curve of PSR J0437−4715.

scholarly journals How ‘Networked’ are Online Collaborative Concept-Maps? Introducing Metrics for Quantifying and Comparing the ‘Networkedness’ of Collaboratively Constructed Content

2020 ◽  
Vol 10 (10) ◽  
pp. 267
Author(s):  
Noa Sher ◽  
Carmel Kent ◽  
Sheizaf Rafaeli
Keyword(s):  
Network Analysis ◽  
Large Scale ◽  
Concept Maps ◽  
Quality Measures ◽  
Thought Process ◽  
Academic Settings ◽  
New Challenges ◽  
Complex Phenomena

With the growing role of online multi-participant collaborations in shaping the academic, professional, and civic spheres, incorporating collaborative online practices in educational settings has become imperative. As more educators include such practices in their curricula, they are faced with new challenges. Assessment of collaborations, especially in larger groups, is particularly challenging. Assessing the quality of the collaborative “thought process” and its product is essential for both pedagogical and evaluative purposes. While traditional quantitative quality measures were designed for individual work or the aggregated work of individuals, capturing the complexity and the integrative nature of high-quality collaborative learning requires novel methodologies. Network analysis provides methods and tools that can identify, describe, and quantify non-linear and complex phenomena. This paper applies network analysis to the content created by students through large-scale online collaborative concept-mapping and explores how these can be applied for the assessment of the quality of a collective product. Quantitative network structure measures are introduced for this purpose. The application and the affordances of these metrics are demonstrated on data from six large-group online collaborative discussions from academic settings. The metrics presented here address the organization and the integration of the content and enable a comparison of collaborative discussions.

scholarly journals GJ 357 b

2020 ◽  
Vol 641 ◽  
pp. A113
Author(s):  
D. Modirrousta-Galian ◽  
B. Stelzer ◽  
E. Magaudda ◽  
J. Maldonado ◽  
M. Güdel ◽  
...  
Keyword(s):  
Large Scale ◽  
Systematic Errors ◽  
Small Mass ◽  
Activity Levels ◽  
M Dwarfs ◽  
X Ray ◽  
History Of ◽  
Age Relations ◽  
M Dwarf ◽  
Atmospheric Mass

Aims. In this paper we present a deep X-ray observation of the nearby M dwarf GJ 357 and use it to put constraints on the atmospheric evolution of its planet, GJ 357 b. We also analyse the systematic errors in the stellar parameters of GJ 357 in order to see how they affect the perceived planetary properties. Methods. By comparing the observed X-ray luminosity of its host star, we estimate the age of GJ 357 b as derived from a recent XMM-Newton observation (log Lx [erg s−1] = 25.73), with Lx− age relations for M dwarfs. We find that GJ 357 presents one of the lowest X-ray activity levels ever measured for an M dwarf, and we put a lower limit on its age of 5 Gyr. Using this age limit, we performed a backwards reconstruction of the original primordial atmospheric reservoir. Furthermore, by considering the systematic errors in the stellar parameters, we find a range of possible planetary masses, radii, and densities. Results. From the backwards reconstruction of the irradiation history of GJ 357 b’s we find that the upper limit of its initial primordial atmospheric mass is ~38 M⊕. An initial atmospheric reservoir significantly larger than this may have survived through the X-ray and ultraviolet irradiation history, which would not be consistent with current observations that suggest a telluric composition. However, given the relatively small mass of GJ 357 b, even accreting a primordial envelope ≳10 M⊕ would have been improbable as an unusually low protoplanetary disc opacity, large-scale migration, and a weak interior luminosity would have been required. For this reason, we discard the possibility that GJ 357 b was born as a Neptunian- or Jovian-sized body. In spite of the unlikelihood of a currently existing primordial envelope, volcanism and outgassing may have contributed to a secondary atmosphere. Under this assumption, we present three different synthetic IR spectra for GJ 357 b that one might expect, consisting of 100% CO2, 100% SO2, and 75% N2, 24% CO2 and 1% H2O, respectively. Future observations with space-based IR spectroscopy missions will be able to test these models. Finally, we show that the uncertainties in the stellar and planetary quantities do not have a significant effect on the estimated mass or radius of GJ 357 b.

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