Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic diversity and population structure in Harpadon nehereus based on sequence-related amplified polymorphism markers

In this study, the genetic diversity among ten populations of the Bombay duck was studied on the basis of sequence-related amplified polymorphism (SRAP). The ten populations were collected from the East China Sea and South China Sea areas. A total of 98 loci were obtained from 292 individuals using eight SRAP primers. The average proportion of polymorphic loci, genetic diversity (H), and Shannon’s information index were 75.20%, 0.2478, and 0.3735, respectively. Nei’s genetic distance and Shannon’s information index between the ten populations ranged from 0.0410 to 0.3841 and from 0.2396 to 0.4506, and the averages Nei’s gene diversity index (H = 0.2478) and Shannon’s information index (I = 0.3735) at the population level were high. AMOVA showed that most of the variation was within populations (71.74%), and only 28.26% of the variation was between populations. The neighbor-joining tree based on genetic distance revealed that significant genealogical structure existed throughout the examined range of the Bombay duck. The results demonstrated that SRAP marker was an effective tool for the assessment of genetic
A unifying explanation of complex frequency spectra of γ Dor, SPB and Be stars: combination frequencies and highly non-sinusoidal light curves

There are many Slowly Pulsating B (SPB) stars and γ Dor stars in the Kepler mission data set. The light curves of these pulsating stars have been classified phenomenologically into stars with symmetric light curves and with asymmetric light curves. In the same effective temperature ranges as the γ Dor and SPB stars, there are variable stars with downward light curves that have been conjectured to be caused by spots. Among these phenomenological classes of stars, some show ‘frequency groups’ in their amplitude spectra that have not previously been understood. While it has been recognized that non-linear pulsation gives rise to combination frequencies in a Fourier description of the light curves of these stars, such combination frequencies have been considered to be a only a minor constituent of the amplitude spectra. In this paper, we unify the Fourier description of the light curves of these groups of stars, showing that many of them can be understood in terms of only a few base frequencies, which we attribute to g-mode pulsations, and combination frequencies, where sometimes a very large number of combination frequencies dominate the amplitude spectra. The frequency groups seen in these stars are thus tremendously simplified. We show observationally that the combination frequencies can have amplitudes greater than the base frequency amplitudes, and we show theoretically how this arises. Thus for some γ Dor and SPB stars, combination frequencies can have the highest observed amplitudes. Among the B stars are pulsating Be stars that show emission lines in their spectra from occasional ejection of material into a circumstellar disc. Our analysis gives strong support to the understanding of these pulsating Be stars as rapidly rotating SPB stars, explained entirely by g-mode pulsations.
Changes in baseline growth and maturation parameters of Northwest Atlantic porbeagle, Lamna nasus, following heavy exploitation

We tested for density-dependent changes in growth and maturation of Northwest Atlantic porbeagle (Lamna nasus) after the population declined by 75%-80% from fishing. Vertebrae and reproductive data collected from the virgin (1961-1966) and exploited (1993-2004) populations were analysed to test for differences in growth rate and age and length at maturity between the time periods. We detected significant differences between reparameterized von Bertalanffy growth models for each period, using likelihood ratio tests. Beyond an age of 7 years, mean length at age was greater during 1993-2004 than during 1961-1966. Between 1961-1963 and 1999-2001, length at maturity de- creased in males (from 179 to 174 cm curved fork length (CFL)) and was invariant in females (216 cm CFL), whereas age at maturity declined in both males (from 8 to 7 years) and females (from 19 to 14 years). An analysis of por- beagle temperature associations indicated that sharks occupied comparable temperature conditions during the mid-1960s and 1990s, ruling out the possibility of temperature-induced growth changes. The observed increase in growth rate and decrease in age at maturity following exploitation support the hypothesis of a compensatory density-dependent growth response.
MOJAVE XVI: Multiepoch Linear Polarization Properties of Parsec-scale AGN Jet Cores

We present an analysis of the core linear polarization properties of 387 parsec-scale active galactic nuclei (AGNs) jets. Using 15 GHz VLBA data, we revisit the conclusions of the first paper in this series with multiepoch measurements and more detailed analysis of a larger AGN sample that spans a broader range of synchrotron peak frequencies. Each AGN has been observed for at least five epochs between 1996 and 2017. We find that BL Lac objects have core electric vector position angles (EVPAs) that tend toward alignment with the local jet direction; compared to flat spectrum radio quasars, their EVPAs are also less variable over time. The AGN cores that are most fractionally polarized and least variable in polarization have EVPAs that are closely aligned with the local jet direction; they also have low variability in EVPA. These results support the popular model of a standing transverse shock at the base of the jet that collimates the jet magnetic field perpendicular to the jet direction, increasing the fractional polarization and leading to greater polarization stability over time. High-synchrotron-peaked BL Lac objects form a low luminosity, low fractional polarization population. The five narrow line Seyfert 1 galaxies in our sample have low fractional polarization and large EVPA-jet misalignments. Although AGNs detected at γ-rays are thought to be more Doppler boosted than nondetected AGNs, we find no significant differences in fractional polarization based on detection by Fermi-LAT; the γ-loud AGNs are, however, more variable in core EVPAs.
ModelSpace: Visualizing the Trails of Data Models in Visual Analytics Systems

User interactions with visualization systems have been shown to encode a great deal of information about the the users’ thinking processes, and analyzing their interaction trails can teach us more about the users, their approach, and how they arrived at insights. This deeper understanding is critical to improving their experience and outcomes, and there are tools available to visualize logs of interactions. It can be difficult to determine the structurally interesting parts of interaction data, though, like what set of button clicks constitutes an action that matters. In the case of visual analytics systems that use machine learning models, there is a convenient marker of when the user has significantly altered the state of the system via interaction: when the model is updated based on new information. We present a method for numerical analytic provenance using high-dimensional visualization to show and compare the trails of these sequences of model states of the system. We evaluate this approach with a prototype tool, ModelSpace, applied to two case studies on experimental data from model-steering visual analytics tools. ModelSpace reveals individual user’s progress, the relationships between their paths, and the characteristics of certain regions of the space of possible models.
Semiparametric Estimation of Multivariate GARCH Models

The paper introduces a new simple semiparametric estimator of the conditional variance covariance and correlation matrix (SP-DCC). While sharing a similar sequential approach to existing dynamic conditional correlation (DCC) methods, SP-DCC has the advantage of not requiring the direct parameterization of the conditional covariance or correlation processes, therefore also avoiding any assumption on their long-run target. In the proposed framework, conditional variances are estimated by univariate GARCH models, for actual and suitably transformed series, in the first step; the latter are then nonlinearly combined in the second step, according to basic properties of the covariance and correlation operator, to yield nonparametric estimates of the various conditional covariances and correlations. Moreover, in contrast to available DCC methods, SP-DCC allows for straightforward estimation also for the non-symultaneous case, i.e., for the estimation of conditional cross-covariances and correlations, displaced at any time horizon of interest. A simple ex-post procedure, to ensure well behaved conditional covariance and correlation matrices, grounded on nonlinear shrinkage, is finally proposed. Due to its sequential implementation and scant computational burden, SP-DCC is very simple to apply and suitable for the modeling of vast sets of conditionally heteroskedastic time series.
THE IMPLICATIONS OF "USER TOOLKITS FOR INNOVATION"

“User toolkits for innovation” was recently proposed as a means to eliminate (costly) iterations of need-related information between users and producers in the product development process. We examine the implications of different levels of consumer involvement in product development to learn what happens when firms pass design tasks on to consumers. We explore this issue by studying the relation between user toolkits and firms’ need to support their consumers. An analysis of 78 computer games products suggests that a share of the costs firms save on information acquisition by the toolkits approach may eventually re-emerge as costs in consumer support. Finally, a potential solution to the costly support needs is outlined; namely the establishment of consumer-consumer interaction. A study of Westwood Online shows that consumers are able to substitute firms’ costly support efforts - consumer interaction facilities can enhance the outcomes of the toolkit approach.
Online Prediction of Nonlinear Signal Processing Problems Based Kernel Adaptive Filtering

—This paper presents two of the most knowing kernel adaptive ﬁltering (KAF) approaches, the kernel least mean squares and the kernel recursive least squares, in order to predict a new output of nonlinear signal processing. Both of these methods implement a nonlinear transfer function using kernel methods in a particular space named reproducing kernel Hilbert space (RKHS) where the model is a linear combination of kernel functions applied to transform the observed data from the input space to a high dimensional feature space of vectors, this idea known as the kernel trick. Then KAF is the developing ﬁlters in RKHS. We use two nonlinear signal processing problems, Mackey Glass chaotic time series prediction and nonlinear channel equalization to ﬁgure the performance of the approaches presented and ﬁnally to result which of them is the adapted one.
Efficient Dimensionality Reduction for Big Data Using Clustering Technique

— Clustering is unsupervised classification of patterns (observations, data items, or feature vectors) into teams (clusters). The drawbacks of clustering has been addressed in several contexts by researchers in several disciplines and so reflects its broad charm and quality in concert of the steps in exploratory data analysis. Clustering is useful in several exploratory pattern analysis, grouping, machine learning and making decisions as well as situations including data mining, document retrieval, image segmentation and pattern classi fication. We are living in a digital world. Every day, people generate massive amount of data and store it, for further analysis and management. The amount of knowledge in our world has been exploding. Big Data refers to extremely large datasets that may be analyzed computationally to reveal patterns, trends, and associations especially relating to human behavior and interactions. Due to the short growth of such information, solutions need to be studied so as to handle and extract price and information from these data sets. Therefore an analysis of the different classes of available clustering techniques with big datasets may provide significant and useful conclusions. The proposed system is to study and analyze some of the popular existing clustering techniques and impact of dimensionality reduction on Big Data.
Award FA 9550-10-1-0383 : Learning Representation and Control in Markov Decision Processes : Final Report

Much past work in control learning, such as approximate dynamic programming [4] or reinforcement learning [5] has assumed that the underlying representation is hand-engineered. In the past several years, we have undertaken a research program exploring a variety of methods for automatically constructing basis functions, applying ideas from manifold learning in machine learning and harmonic analysis. In this paper, we briefly survey a variety of directions that are emerging in the community on frameworks for automatically learning both representation and control in Markov decision processes. The approaches can broadly be divided into two classes of methods: those that construct basis functions that are sensitive to the reward function and the specific policy being followed [3], versus approaches that depend only on the geometry of the state (action) space [2]. These two approaches can both be viewed as constructing representations by analyzing the effect of operators on the space of functions on a state (action) space. Broadly, there are two general principles for constructing representations in this manner: dilation methods, which include Krylov bases as well as multiscale wavelet methods [1], and diagonalization methods which work by constructing eigenvectors (or eigenfunctions). Both these approaches essentially can be viewed as finding invariant subspaces of the operator. We will describe an algorithmic framework for simultaneously learning representation and control called representation policy iteration (RPI). This framework modifies the traditional policy iteration algorithm by including an outer loop that constructs basis functions that are either reward-sensitive or independent of rewards. A variety of operators can be used to construct basis functions: one approach is to use a graph connecting nearby points on the sampled state (action) space manifold; another approach is to construct an approximation of the Bellman backup operator. Diagonalization methods work with self-adjoint (symmetric) operators. Dilation methods include multiscale diffusion wavelet
Asymmetric Spotty Patterns for the Gray–Scott Model in R2

In this paper, we rigorously prove the existence and stability of asymmetric spotty patterns for the Gray–Scott model in a bounded two‐dimensional domain. We show that given any two positive integers k1, k2, there are asymmetric solutions with k1 large spots (type A) and k2 small spots (type B). We also give conditions for their location and calculate their heights. Most of these asymmetric solutions are shown to be unstable. However, in a narrow range of parameters, asymmetric solutions may be stable.
6683 Optimal Unbiased Steady-State Relaxometry with Phase-Cycled Variable Flip Angle ( PCVFA ) by Automatic Computation of the Cramér-Rao Lower Bound

Fig. 2: T1 (top) and T2 (bottom) CoVs of the optimal GM protocols over a tissue range for columns (a) DESPOT2 and (b) PCVFA. denotes the target GM tissue. 6683 Optimal Unbiased Steady-State Relaxometry with Phase-Cycled Variable Flip Angle (PCVFA) by Automatic Computation of the Cramér-Rao Lower Bound Jason Su and Brian Rutt Electrical Engineering, Stanford University, Stanford, CA, United States, Radiology, Stanford University, Stanford, CA, United States
Overseas Travel Grant : Case for Support Theory of Visualization : From an Information-Theoretic Framework to an Information-Theoretic System Part 1 : Track Record

From 1 August 2017, Oxford e-Research Centre (where Professor Min Chen has been based since 2011) became part of Department of Engineering Science. Oxford Engineering Science has an international reputation for its research in all the major branches of engineering and its research groups under the theme of “Information, Control & Vision” are a leading force in developing data science and knowledge engineering. The broad collection of existing research activities (such as machine learning, driverless cars, economic modelling, video and image understanding, and so on) and the upcoming creation of the Oxford Information Science Institute will provide this proposal with an exceptional backdrop as well as the inspiration for making fundamental breakthroughs.
Modularity and OWL

4 Modularization Algorithms 15 4.1 Ad hoc algorithms . 15 4.1. 1 PromptFactor algorithm . 15 4.1. 2 Structure-based partitioning algorithm . 15 4.1. 3 GALEN segmentation algorithm . 16 4.2 Formal algorithms . 16 4.2. 1 CEL and MEX algorithms . 17 4.2. 2 Cuenca Grau et al. (2006) algorithm . 17 4.2. 3 Locality-based algorithm . 18
Enamel-Related Gene Products in Calvarial Development

Brodie Laboratory for Craniofacial Genetics, University of Illinois at Chicago, Illinois USA 60612 Department of Orthodontics, University of Illinois at Chicago, Illinois, USA 60612 UIC Department of Oral Biology, College of Dentistry, Illinois USA 60612 National Institute for Dental and Craniofacial Research, National Institutes of Health, Bethesda, MD, USA Department of Anatomy and Cell Biology, University of Pennsylvania School of Dental Medicine, Philadelphia, PA, USA
The Dark Side of International Trade Agreements

While increasing trade and foreign direct investment, international trade agreements create winners and losers. Our paper identifies the distributional consequences of international trade agreements at the firm level. We contend that preferential trade agreements and the World Trade Organization expand the activities of the largest and most productive firms by lowering trade costs. We test our argument using confidential firm-level data covering the near universe of U.S. foreign direct investment, as well as preferential and most favored nation tari↵ data. We find that by lowering trade costs, both preferential liberalization and, to a lesser extent, multilateral liberalization reallocate economic gains from the least to the most competitive firms. Our results unveil the “dark side” of international trade institutions: concentrated benefits largely for the most powerful actors in the global economy and sharp increases in market concentration.
A Novel Framework for Computing Unique People Count from Monocular Videos

I am a 5th year PhD student in the department of Computing Science in University of Alberta. I have passed my candidacy examination last year. I am currently in the final stage of my research and planning to defend by next semester. In my PhD thesis, I have developed a novel people counting algorithm for computing unique people count from monocular videos. The algorithm has the capability of handling severe occlusion in addition to computing unique people count with exorbitant accuracy. Also it is online in nature, and does not accumulate error over time. I have performed extensive experiments with the proposed algorithm on four standard datasets the UCSD dataset (Chan et al., 2008), which consist of a full one hour video of 25 ,656 frames, the FUDAN dataset (Tan et al., 2011) consisting of 1500 frames, the LHI dataset (Cong et al., 2009) which has 12 videos captured at different camera angles (90 degree, 65 degree and 40 degree) and of duration between 5 minutes and 15 minutes, and the PETS 2009 dataset (Krahnstoever et al., 2008) consisting of multiple camera views, targeted at the evaluation of various surveillance applications. The algorithm has produced more than 95% accuracy for most of these videos.
A Group Decision-Making Model Based on Regression Method with Hesitant Fuzzy Preference Relations

In recent years, the decision-making models with hesitant fuzzy preference relations (HFPRs) have received a lot of attention by some researchers. Meanwhile, the previous studies normally adopt normalization technical means to ensure the same number for all elements, which biases original information of decision-makers. In order to overcome this problem, in this paper, the multiplicative consistency of HFPRs is defined and the highest consistent reduced HFPRs are obtained by means of fuzzy linear programming method from given HFPRs. The proposed regression method eliminates the unreasonable information and retains the reasonable information from a given HFPR. In addition, the proposed method overcomes drawbacks of Zhu and Xu’s regression method and is more simple and effective. On account of the obtained reduced HFPRs by the proposed regression method, a GDM model is established. Finally, a supplier selection problem was researched to present the effectiveness and pragmatism of the proposed approach, which proved that the method could offer beneficial insights into the GDM procedure.
Accuracy of Distributed Optical Fiber Temperature Sensing for Use in Leak Detection of Subsea Pipelines

AbstractAccurate and rapid detection of leaks is important for subsea oil pipelines to minimize environmental risks and operational/repair costs. Temperature-sensing optical fiber cables can provide economic, near real-time sensing of leaks in subsea oil pipeline networks. By employing optical time domain reflectometry and detecting the Brillouin scattered components from a laser source, the temperature gradients can be detected at any location along an optical fiber cable attached to the oil pipeline. The feasibility of such technology has been established in the literature along with a case study on a land-based pipeline. In this paper the accuracy of an optical fiber-based temperature sensing system is investigated. A mathematical model that simulates the process of temperature sensing is developed and the results are presented. An experimental investigation is carried out with two different laboratory setups to establish the spatial resolution and accuracy of the optical fiber cable detection system, .
Recent studies overestimate colonization and extinction events for Adelie Penguin breeding colonies

ABSTRACT Modeling metapopulation dynamics is potentially a useful and powerful tool for ecologists and conservation biologists. However, the key processes driving metapopulation dynamics are colonization and extinction events, which are notoriously difficult to study. A recent global assessment of Adelie Penguin (Pygoscelis adeliae) breeding populations reported multiple potential colonization and extinction events from occupancy observations using recent high-resolution satellite imagery and examination of historical occupancy literature, but emphasized that the events should be confirmed with direct observations. We evaluated these conclusions by developing a comprehensive database of direct observations of Adelie Penguin breeding site occupancy across East Antarctica. The database allowed 16 of 19 proposed colonization and extinction events in this region to be evaluated using direct observations that were concurrent in space and time (same breeding season) with observations from satellite imagery. We concluded that none of the 16 proposed colonization and extinction events had occurred. One true extinction event may have been correctly identified from satellite imagery, but a conclusive evaluation was not possible because the direct and satellite observations were not concurrent in time. The evaluation identified several sources of error in observations from satellite imagery, including errors of omission through failure to detect small colonies and errors of commission through misidentification of other biological and physical features as Adelie Penguin guano. The occupancy database corrected issues of poor precision and accuracy in locations of historical occupancy data. Our results improve understanding of key parameters for Adelie Penguin metapopulation dynamics and facilitate improved interpretation of satellite imagery in the future.