Miracle-dz/UniFAR-FTUs huggingface dataset:
ZTF Early Observations of Type Ia Supernovae. I. Properties of the 2018 Sample

Early-time observations of Type Ia supernovae (SNe Ia) are essential to constrain the properties of their progenitors. In this paper, we present high-quality light curves of 127 SNe Ia discovered by the Zwicky Transient Facility (ZTF) in 2018. We describe our method to perform forced point-spread function photometry, which can be applied to other types of extragalactic transients. With a planned cadence of six observations per night (three g + three r), all of the 127 SNe Ia are detected in both g and r bands more than 10 days (in the rest frame) prior to the epoch of g-band maximum light. The redshifts of these objects range from z = 0.0181 to 0.165; the median redshift is 0.074. Among the 127 SNe, 50 are detected at least 14 days prior to maximum light (in the rest frame), with a subset of nine objects being detected more than 17 days before g-band peak. This is the largest sample of young SNe Ia collected to date; it can be used to study the shape and color evolution of the rising light curves in unprecedented detail. We discuss six peculiar events in this sample: one 02cx-like event ZTF18abclfee (SN 2018crl), one Ia-CSM SN ZTF18aaykjei (SN 2018cxk), and four objects with possible super-Chandrasekhar mass progenitors: ZTF18abhpgje (SN 2018eul), ZTF18abdpvnd (SN 2018dvf), ZTF18aawpcel (SN 2018cir), and ZTF18abddmrf (SN 2018dsx).
A comparison of methods for the quantification of bacterial sulfate reduction in coastal marine sediments

A mathematical model describing vertical sulfate gradients as a function of diffusion, bacterial reduction, and sedimentation is applied to calculate the steady‐state rates of sulfate reduction in coastal sediments. Diffusion coefficients of sulfate are determined experimentally in different sediment types and depths. The rate of sulfate reduction is also determined directly on sediment cores by a radiotracer technique. Experimentally determined rates are compared with those calculated from the model. The results of the model are shown to depend strongly on its assumptions about the kinetics of bacterial sulfate reduction.
Nature, origine et réactivité de la matière organique fossile dans les sols et sédiments : développements et applications de la photoionisation - spectrométrie de masse haute résolution (APPI-QTOF) et couplage avec la chromatograhie d'exclusion stérique (SEC)

Le developpement des outils analytiques pour l'analyse de la matiere organique complexe en geochimie organique a connu de nombreuses avancees ces dernieres annees. Ce developpement a permis de repondre a un grand nombre de questions quant a la composition de la matiere organique. Cependant, beaucoup des points restent encore a elucider comme notamment la caracterisation des fractions de hauts poids moleculaires ainsi que le suivi de la reactivite de la matiere organique. Ce travail de these a eu pour objectif (i) d'adapter les techniques de spectrometrie de masse deja existantes pour l'analyse de la matiere organique fossile (notamment par la selection de la source d'ionisation atmospherique la plus adaptee) mais egalement (ii) de developper un nouveau type de couplage entre la chromatographie d'exclusion sterique (SEC) et la spectrometrie de masse APPI-QTOF pour l'analyse des fractions peu polaires de hauts poids moleculaires. L'adaptation du l'APPI-QTOF a tout d'abord permis de mieux comprendre la reactivite de contaminants organiques polyaromatiques en presence de phases minerales. Le couplage SEC-APPI-QTOF a, quant a lui, permis d'ameliorer les connaissances sur la structure des asphaltenes. Cependant, malgre la « simplification » rendue possible par la SEC, la tres grande quantite d'informations reste difficile a interpreter et prend beaucoup de temps. Un modele mathematique a donc ete developpe base sur des analyses numeriques et statistiques des spectres de masse, permettant de les comparer entre eux afin de distinguer l'origine des echantillons et de suivre l'impact de processus physico-chimiques (alterations naturelles - traitements de remediation).
Prioritizing the Effective Factors on Knowledge Commercialization Using Fuzzy Analytic Hierarchy Process: A Case Study

Purpose: The purpose of the present study is to prioritize the effective factors on knowledge commercialization using fuzzy AHP at Isfahan University. Methodology: This research is an applied type of research that is conducted with mixed data collection method. The research community included 790 managers, executives, researchers and faculty members of the Isfahan University, and using stratified sampling 260 individuals were selected. The data collection tool is a questionnaire. Findings: Using fuzzy method, it was determined that among the triple factors, the contextual factors, and among the investigated sub-criteria, the commercialization culture sub-criteria, the knowledge base and research quality, and innovative infrastructure have the highest importance. In prioritizing the investigated indexes, the index of developing and promoting the commercialization culture and entrepreneurship, processing the results for different purposes, and focusing and considering the needs of market and customer are prioritized. Discussion and Conclusion: Due to the direct relation of the contextual factors with economics and politics, they have the most impact on knowledge commercialization at Isfahan University. Likewise, the content factors due to their relation with knowledge quality and human skills are considered in the second priority. Among the indexes, the index of developing and promotion of the commercialization culture, processing of results for different purposes, and also focusing and considering the needs of the market and the customer had the highest importance. Therefore, culturalization, the use of research results in different dimensions, and moving toward meeting the needs of diverse customers can help the process of knowledge commercialization Isfahan University.
A Report

Background . The present study updates and expands on a 2012 systematic review of the prevalence and risk of violence and the prevalence and risk of physical, mental and sexual health problems among trafficked people. Methods . Systematic review and meta-analysis. Searches of fifteen electronic databases of peer-reviewed articles and doctoral theses were supplemented by reference screening, citation tracking of included articles, and expert recommendations. Studies were included if they reported on the prevalence or risk of violence while trafficked, or the prevalence or risk of physical, mental, or sexual health outcomes among people who have been trafficked. Two reviewers independently screened papers for eligibility and appraised the quality of included studies. Results . Thirty-seven papers reporting on thirty-one studies were identified. The majority of studies were conducted in low and middle-income countries with women and girls trafficked into the sex industry. There is limited but emerging evidence on the health of trafficked men and the health consequences of trafficking into different forms of exploitation. Studies indicate that trafficked women, men and children experience high levels of violence and report significant levels of physical health symptoms, including headaches, stomach pain and back pain. Most commonly reported mental health problems include depression, anxiety and posttraumatic stress disorder. Although serological data on sexually transmitted infections are limited, women and girls trafficked for sexual exploitation self-report symptoms suggestive of a high prevalence of infections. Limitations of the review include methodological weaknesses of primary studies and some differences in definition and operationalisation of trafficking, which hinder comparability and generalisability of the results. Conclusions . There is increasing evidence human trafficking is associated with high prevalence and increased risk of violence and a range of physical and mental health problems. Although more studies have emerged in recent years reporting on the health of trafficked men and people trafficked for forms of exploitation other than in the sex industry, further research is needed in this area. Appropriate interventions and support services to address the multiple and serious medical needs, especially mental health, of trafficked people are urgently needed.
Semi-Analytical Model for Design and Analysis of On-Orbit Servicing Architecture

Robotic on-orbit servicing (OOS) is expected to be a key technology and concept for future sustainable space exploration. This paper develops a semi-analytical model for OOS systems analysis, responding to the growing needs and ongoing trend of robotic OOS. An OOS infrastructure system is considered whose goal is to provide responsive services to the random failures of a set of customer modular satellites distributed in space (e.g., at the geosynchronous equatorial orbit). The considered OOS architecture is comprised of a servicer that travels and provides module-replacement services to the customer satellites, an on-orbit depot to store the spares, and a series of launch vehicles to replenish the depot. The OOS system performance is analyzed by evaluating the mean waiting time before service completion for a given failure and its relationship with the depot capacity. Leveraging the queueing theory and inventory management methods, the developed semi-analytical model is capable of analyzing the OOS system performance without relying on computationally costly simulations. The effectiveness of the proposed model is demonstrated using a case study compared with simulation results. This paper is expected to provide a critical step to push the research frontier of analytical/semi-analytical models development for complex space systems design.
Hybrid connection network for semantic segmentation

In recent years, deep convolutional neural networks like ResNet and DenseNet with short cut connected to each layer can be more accurate and easier to train. Although deep convolutional neural networks show their strength in many computer vision tasks, there is still a challenge to get more precise per-pixel prediction for semantic image segmentation task via deep convolutional neural networks. In this paper, we propose a hybrid connection network architecture for semantic segmentation which consists of an encoder network for extracting different scale feature maps and a decoder network for recovering extracted feature maps with the resolution of the input image. This architecture includes several skip connection paths between encoder and decoder. The paths help to fuse both localization information and global information. We show that our architecture can be quickly trained end-to-end without pre-training on an additional dataset and performs comparable results on semantic segmentation benchmark datasets such as PASCAL VOC 2012.
Southern ( SAIS ) 3-1-2004 Technology Adoption in Complex Systems

Adoption of information technology remains an important consideration in IS research. With the growing number of complex systems being implemented within the organizations, there also exists a need to understand these systems, their adoption and post adoption behavior. This study focuses on these issues. We develop a description of complex technologies. Subsequently we extended the technology acceptance model to include perceived risk of use, as a new construct in the nomological network leading to, post-adoptive exploration of complex technologies.
Modelling and forecasting the money demand in China : Cointegration and nonlinear analysis

This paper deals with the demand for money, including narrow money (M1) and broad money (M2) in China. We use the cointegration and error-correction model to formulate the function of money demand and merge the short-run and long-run equations to give forecasts over different horizons. In particular, we combine very simple artificial neural networks (ANNs) with the cointegration and error-correction model to give a nonlinear model. These models are quarterly models, sampled from the first quarter of 1980 to the fourth quater of 1994, and the multi-step forecasts are from the first quarter of 1990 to the fourth quarter of 1994. Both the fitted values and predictive values for M1 and M2 are satisfactory. Finally, we give forecasts for M2 from the first quarter of 1995 to the second quarter of 1996.
Flash-based Storage Management in Cloud Computing Datacenter Infrastructures

of the Dissertation Flash-based Storage Management in Cloud Computing Datacenter Infrastructures by Zhengyu Yang Doctor of Philosophy in Electrical and Computer Engineering Northeastern University, July 2018 Dr. Ningfang Mi, Advisor A basic credendum of cloud computing can be summarized as: user devices are light terminals to assign jobs and gather results, while those heavy computations are conducted on remote distributed server clusters. This light-terminal-heavy-server structure makes high availability no longer an option, but a requirement in today’s datacenters. Furthermore, when bringing compute and storage capabilities into balance, we find that the biggest challenge here is closing the gap between computing and storage performance to shift storage’s curve back towards Moore’s law [1]. In detail, the time consumed to wait for I/Os is the main cause of idling and wasting CPU resources, since lots of popular cloud applications are I/O intensive, such as video streaming, file sync and backup, and data iteration for machine learning, etc [2]. Thus, storage I/O is the biggest bottleneck in large-scale datacenters. To address this bottleneck, Solid State Drives (SSDs) are widely being deployed as a per-virtual disk, second-level cache of Hard Disk Drives (HDDs) in SSD-HDD hybrid storage systems to improve I/O access performance, ascribing to SSDs’ high I/O throughput and low I/O latency and power consumption. Recently, with the capital expenditure of Flash-based SSDs keeps decreasing and the storage capacity of SSDs keeps increasing, the “sales pitch” of traditional HDDs as backend storage – low cost and large capacity – is no longer that unique, and eventually they will be replaced by low-end SSDs which have large capacity but perform orders of magnitude better than HDDs. As a result, it is widely believed that all-flash multi-tier storage systems will be adopted in the enterprise datacenters running big data platforms in the near future. Therefore, in this dissertation, we focus on flash-based storage resource management for SSD-HDD hybrid, all-flash storage systems and storage optimization in big data platforms, and we aim to achieve high availability by improving both performance and reliability in these storage systems.
Length-Bounded Cuts: Proper Interval Graphs and Structural Parameters

In the presented paper we study the Length-Bounded Cut problem for special graph classes as well as from a parameterized-complexity viewpoint. Here, we are given a graph $G$, two vertices $s$ and $t$, and positive integers $\beta$ and $\lambda$. The task is to find a set of edges $F$ of size at most $\beta$ such that every $s$-$t$-path of length at most $\lambda$ in $G$ contains some edge in $F$. Bazgan et al. conjectured that Length-Bounded Cut admits a polynomial-time algorithm if the input graph $G$ is a~proper interval graph. We confirm this conjecture by showing a dynamic-programming based polynomial-time algorithm. We strengthen the W[1]-hardness result of Dvořak and Knop. Our reduction is shorter, seems simpler to describe, and the target of the reduction has stronger structural properties. Consequently, we give W[1]-hardness for the combined parameter pathwidth and maximum degree of the input graph. Finally, we prove that Length-Bounded Cut is W[1]-hard for the feedback vertex number. Both our hardness results complement known XP algorithms.
Feasibility Evaluation for Condominium Reconstruction by Means of the Application of Discriminant Analysis on Real Estate Data

This paper discusses the application of discriminant analysis on real estate data as a feasible estimation method for condominium reconstruction. The proposed method was used in order to verify its prediction capacity managing data from the real estate and resulting in 89.4% accuracy. This method revealed and highlighted the structure of four explanatory variables in terms of prediction impact. Further, it has been clarified that only three out of these four explanatory variables mentioned above were selected and constantly used in order to explain each analytical model.
The effect of sports activities classes in the physical education

The basic aim of physical education is to, by means of an organized process of education, carry out a positive influence on the psycho-somatic status of schoolchildren and to use the transformations in the somatic status to correct the influence of biological factors which have been determined to be insufficient during the period of intense growth and development. In this research we will try to study the effects of sports activities classes in the form of a third physical education class per week. The sample of participants consisted of sixth graders, all male, who chose basketball as part of their third class of sports activities per week. The sample of participants consisted of two sub-samples, a control and experimental group, which consisted of 50 participants each. The control group, which had two physical education classes per week, took part in the regular physical education classes, while the experimental group, which in addition to two classes of physical education, also took part in one class of sports activities. The obtained results indicate that the experimental group, at the final measuring, achieved better results than the control group, which leads us to the conclusion that the contents of sports activities class (basketball) during the experimental period which lasted during one school semester, contributed to the improvement in the motor skills of the sixth graders.
SOLVING LINEAR LEAST SQUARES PROBLEMS BY GRAM-SCHMIDT ORTHOGONALIZATION

1. Introduct ion . L e t A b e a g i v e n m x n r ea l m a t r i x of r a n k n, m > n, a n d b a g i v e n m x 1 r e a l v e c t o r . T h e n t h e r e e x i s t s a u n i q u e v e c t o r x w h i c h so lves t h e l e a s t s q u a r e s p r o b l e m of m i n i m i z i n g ltb A x l t ~ • I t is we l l k n o w n t h a t t h e s o l u t i o n x sa t i s f i e s t h e c o n d i t i o n A ~ ' ( b A x ) = O , (1 . I ) i . e . t h e r e s i d u a l v e c t o r r = b A x is o r t h o g o n a l t o t h e c o l u m n s of A . I t fo l lows t h a t w e c a n c o m p u t e x f r o m t h e n o r m a l e q u a t i o n s A T A x = A r b . (1.2) W e d e f i n e fo l l owing B a u e r [1] t h e ( co lumnw ise ) c o n d i t i o n of t h e r e c t a n g u l a r m a t r i x A t o be c o n d ( A ) = m a x []Axll/min []Ax][. I~1=I II~i=I T h e c o n d i t i o n n u m b e r c o r r e s p o n d i n g t o t h e L 2 n o r m is d e n o t e d b y n(A). I n s e c t i o n 7 we wi l l show t h a t u n d e r s o m e r e s t r i c t i o n s , z (A) c a n b e con-
Example Based Learinig for View-Based Human Face Detection 6

Finding human faces automatically in an image is a difficult yet important first step to a fully automatic face recognition system. It is also an interesting academic problem because a successful face detection system can provide valuable insight on how one might approach other similar object and pattern detection problems. This paper presents an example-based learning approach for locating vertical frontal views of human faces in complex scenes. The technique models the distribution of human face patterns by means of a few view-based "face" and "non-face" prototype clusters. At each image location, a difference feature vector is computed between the local image pattern and the distribution-based model. A trained classifier determines, based on the difference feature vector, whether or not a human face exists at the current image location. We show empirically that the prototypes we choose for our distribution-based model, and the distance metric we adopt for computing difference feature vectors, are both critical for the success of our
A Physics-Based Attack Detection Technique in Cyber-Physical Systems: A Model Predictive Control Co-Design Approach

In this paper a novel approach to co-design controller and attack detector for nonlinear cyber-physical systems affected by false data injection (FDI) attack is proposed. We augment the model predictive controller with an additional constraint requiring the future—in some steps ahead—trajectory of the system to remain in some time-invariant neighborhood of a properly designed reference trajectory. At any sampling time, we compare the real-time trajectory of the system with the designed reference trajectory, and construct a residual. The residual is then used in a nonparametric cumulative sum (CUSUM) anomaly detector to uncover FDI attacks on input and measurement channels. The effectiveness of the proposed approach is tested with a nonlinear model regarding level control of coupled tanks.
Running head: INTERPRETATION OF STRUCTURE COEFFICIENTS

The increased use of multiple regression analysis in research warrants closer examination of the coefficients produced in these analyses, especially ones which are often ignored, such as structure coefficients. Structure coefficients are bivariate correlation coefficients between a predictor variable and the synthetic variable. When predictor variables are correlated with each other, regression results may be seriously distorted by failure to interpret structure coefficients. Structure coefficients have analogues in all analyses (e.g., canonical analysis, factor analysis, and discriminant analysis) and should be interpreted in all analyses. Several examples of research in which not examining structure coefficients led to misinterpreting results are cited. A small heuristic example is presented to provide a concrete example of how interpretation of regression results might differ when predictor variables are correlated with each other. The astute researcher should examine both beta weights and structure coefficients when interpreting regression results with correlated predictor variables. (Contains 1 figure, 1 table, and 27 references.) (Author/SLD) *********************************************************************** Reproductions supplied by EDRS are.the best that can be made from the original document. ***********************************************************************
Microsatell ite DNA markers to assess population structure of red tailed barb

Received Dec. 28 ,2003 ;accepted Jan. 31 ,2004 3 This research was funded by a grant from World Bank (No. MM2III\ 18) 33 Corresponding author. E2mail :nbfgr @sancharnet . in ;kulvin 100 @hotmail. com 333 NBFGR Cochin Unit ,CMFRI Campus , PB # 1603 , Ernakulam , Kochi :682018 , Kerala , India. E2mail :nbfgrcochin @rsnl. net 3333 Present Address :BGRRP ,World Fish Center ,PO Box 500 GPO ,10670 ,Penang ,Malaysia. E2mail :a. ponniah @cgiar. org ν 2003 动物学报 Acta Zoologica Si nica Microsatell ite DNA markers to assess population structure of red tailed barb Gonoproktopterus curmuca 3
Improved Regret Bounds for Projection-free Bandit Convex Optimization

We revisit the challenge of designing online algorithms for the bandit convex optimization problem (BCO) which are also scalable to high dimensional problems. Hence, we consider algorithms that are \textit{projection-free}, i.e., based on the conditional gradient method whose only access to the feasible decision set, is through a linear optimization oracle (as opposed to other methods which require potentially much more computationally-expensive subprocedures, such as computing Euclidean projections). We present the first such algorithm that attains $O(T^{3/4})$ expected regret using only $O(T)$ overall calls to the linear optimization oracle, in expectation, where $T$ is the number of prediction rounds. This improves over the $O(T^{4/5})$ expected regret bound recently obtained by \cite{Karbasi19}, and actually matches the current best regret bound for projection-free online learning in the \textit{full information} setting.