Miracle-dz/UniFAR-FTUs huggingface dataset:
Scale Length of Disk Galaxies

Abstract Disk scale length rd and central surface brightness μ0 for a sample of 29955 bright disk galaxies from the Sloan Digital Sky Survey have been analyzed. Cross correlation of the SDSS sample with the LEDA catalogue allowed us to investigate the variation of the scale lengths for different types of disk/spiral galaxies and present distributions and typical trends of scale lengths all the SDSS bands with linear relations that indicate the relation that connect scale lengths in one passband to another. We use the volume corrected results in the r-band and revisit the relation between these parameters and the galaxy morphology, and find the average values 〈rd〉 = 3.8 ± 2.1 kpc and 〈μ0〉 = 20.2 ± 0.7 mag arcsec−2. The derived scale lengths presented here are representative for a typical galaxy mass of 1010.8 M⊙, and the RMS dispersion is larger for more massive galaxies. We analyse the rd–μ0 plane and further investigate the Freeman Law and confirm that it indeed defines an upper limit for μ0 in bright disks (rmag < 17.0), and that disks in late type spirals (T ≥ 6) have fainter central surface brightness. Our results are based on a sample of galaxies in the local universe (z < 0.3) that is two orders of magnitudes larger than any sample previously studied, and deliver statistically significant results that provide a comprehensive test bed for future theoretical studies and numerical simulations of galaxy formation and evolution.
A New Approach for Dense Depth Image Recovery

The dense depth image recovery technology has attracted increasing attention in the field of Unmanned Ground Vehicle (UGV). A depth map at the resolution of a camera image simplifies image segmentation, object tracking, classification and recognition. This paper presents a method to generate dense depth image using bilateral filter framework and then refine it with Markov Random Field (MRF). Experimental results on the monocular images and sparse laser point clouds data from the KITTI raw datasets show that the dense depth image can be acquired under the complex scene in rural environments.
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing

Efficient application scheduling is critical for achieving high performance in heterogeneous computing environments. The application scheduling problem has been shown to be NP-complete in general cases as well as in several restricted cases. Because of its key importance, this problem has been extensively studied and various algorithms have been proposed in the literature which are mainly for systems with homogeneous processors. Although there are a few algorithms in the literature for heterogeneous processors, they usually require significantly high scheduling costs and they may not deliver good quality schedules with lower costs. In this paper, we present two novel scheduling algorithms for a bounded number of heterogeneous processors with an objective to simultaneously meet high performance and fast scheduling time, which are called the Heterogeneous Earliest-Finish-Time (HEFT) algorithm and the Critical-Path-on-a-Processor (CPOP) algorithm. The HEFT algorithm selects the task with the highest upward rank value at each step and assigns the selected task to the processor, which minimizes its earliest finish time with an insertion-based approach. On the other hand, the CPOP algorithm uses the summation of upward and downward rank values for prioritizing tasks. Another difference is in the processor selection phase, which schedules the critical tasks onto the processor that minimizes the total execution time of the critical tasks. In order to provide a robust and unbiased comparison with the related work, a parametric graph generator was designed to generate weighted directed acyclic graphs with various characteristics. The comparison study, based on both randomly generated graphs and the graphs of some real applications, shows that our scheduling algorithms significantly surpass previous approaches in terms of both quality and cost of schedules, which are mainly presented with schedule length ratio, speedup, frequency of best results, and average scheduling time metrics.
Correlation between histological grade and positron emission tomography parameters in cervical carcinoma

The aim of the present study was to evaluate the changes in cervical cancer glucose metabolism for different levels of cellular differentiation. The metabolic activity was measured by standardized uptake value (SUV), SUV normalized to lean body mass, metabolic tumor volume and total lesion glycolysis using fluorine-18 fluorodeoxyglucose positron emission tomography (PET). A correlation study of these values could be used to facilitate therapeutic choice and to improve clinical practice and outcome. This study considered 32 patients with diagnosed cervical cancers, at different International Federation of Gynecology and Obstetrics stages. Glucose metabolism was assessed by PET examination, and histological specimens were examined to determine their initial grade of differentiation. A correlation study of these values was evaluated. Histological examination showed that all cases were of squamous cell carcinoma. Regarding the differentiation of the tumor, 19 well- to moderately-differentiated tumors and 13 poorly-differentiated tumors were determined. Negative findings for correlations between metabolic parameters and initial grade of histological differentiation were found, and considering that histological grade has been shown to have no consistent prognostic value in cervical cancer treatment, PET imaging could play a significant role in cervical cancer prognosis.
Hook2 is involved in the morphogenesis of the primary cilium

Hook2 partitions between the Golgi apparatus and the centrosome, and its depletion hinders ciliogenesis after mother centriole maturation without Golgi breakdown. Hook2 interacts with PCM1 and Rab8a, and Hook2-depleted cells can be forced to grow primary cilia by overexpressing GFP::Rab8a, indicating that Rab8a acts downstream of Hook2 and PCM1.
INTEGRAL METHOD FOR A CAPACITANCE MICROSCOPE THAT IS BASED ON CYLINDRIC METALLIC SURFACES

In this work, we consider the problem of obtaining a capacitive image by scanning a “one-dimensional” surface of a closed conductor of arbitrary geometry. To solve our problem we propose a novel integral numerical method. The method is applied to different geometries by considering deterministic surfaces as complex as those with a fractal structure and random rough surfaces with Gaussian statistics. We find that the images obtained by simulating a prototype of a capacitive microscope, strongly depend on the interaction between the object and the probe. Despite this interaction, important information can be obtained regarding the statistical properties of the random roughness of the object surface.
Penerapan Inquiry Based Learning berbantuan Peraga Manipulatif dalam Meningkatkan Pemahaman Konsep Matematika pada Materi Geometri Mahasiswa PGSD Universitas Muria Kudus

Mahasiswa PGSD Universitas Muria Kudus berasal dari berbagai jenis latar belakang jurusan seperti SMA/MA IPA, IPS, Bahasa serta SMK dari berbagai jurusan. Tidak semua mahasiswa tersebut mempunyai pemahaman yang kuat tentang matematika. Sebagian besar dari mereka yang berasal dari jurusan non eksak mengalami kesulitan dalam memahami konsep matematika. Miskonsepsi terjadi paling banyak pada lingkup materi geometri. Hal ini menjadi tantangan bagi para Dosen eksak untuk mengantarkan materi – materi matematika agar lebih mudah dipahami oleh semua mahasiswa. Tujuan dari penelitian ini adalah untuk melihat bagaimana Inquiry based Learning berbantuan peraga manipulatif meningkatkan pemahaman konsep matematika mahasiswa PGSD Universitas Muria Kudus. Penelitian ini merupakan penelitian tindakan yang dilaksanakan dengan dua siklus dengan urutan tahapan perencanaan, tindakan, pengamatan dan refleksi. Subjek penelitiannya adalah mahasiswa 2G. Data dikumpulkan dengan observasi dan tes. Hasil penelitian menunjukkan bahwa Inquiry based Learning berbantuan peraga manipulatif mampu meningkatkan pemahaman konsep matematika mahasiswa PGSD Universitas Muria Kudus. Peningkatan pemahaman konsep terlihat dari: 1) rata – rata hasil tes meningkat dari 71,33 pada siklus 1 menjadi 82,45 pada siklus 2, 2) rata – rata aktivitas pembangunan konsep meningkat dari 2,65 dengan kriteria baik menjadi 3,25 dengan kriteria baik. Dari penelitian ini, dapat disimpulkan bahwa inquiry based learning berbantuan peraga manipulatif mampu meningkatkan kemampuan pemahaman konsep matematika lingkup materi geometri pada mahasiswa PGSD, model inquiry based learning berbantu peraga manipulatif dapat juga digunakan untuk pembangunan konsep matematika lingkup materi lain seperti bilangan, pengukuran, statistika dan pengolahan data.
Puzzling wind properties of young massive stars in SMC-N81

We present a quantitative study of massive stars in the High Excitation Blob N81, a compact star forming region in the SMC. The stellar content was resolved by HST, and STIS was used to obtain medium resolution spectra. The qual- itative analysis of the stellar properties presented in Heydari-Malayeri et al. (2002a) is extended using non-LTE spherically extended atmosphere models including line-blanketing computed with the code CMFGEN (Hillier & Miller 1998), and the wind properties are investigated. The main results are the following: - The SMC-N81 components are young (∼0-4 Myrs) O stars with effective temperatures compatible with medium to late subtypes and with luminosities lower than those of average Galactic O dwarfs, rendering them possible ZAMS candidates. - The winds are extremely weak: with values of the order of 10 −8 /10 −9 Myr −1 the mass loss rates are lower than observed so far for Galactic dwarfs. Only the recent study of SMC stars by Bouret et al. (2003) show the same trend. The modified wind momenta ( u M v∞ √ R) are also 1 to 2 orders of magnitude lower than observed for Galactic stars. Both the mass loss rates and the modified wind momenta are lower than the predictions of the most recent hydrodynamical models. The accuracy of the UV based mass loss rate determination, relying in particular on the predicted ionisation fractions, are carefully examined. We find that u M could be underestimated by a factor of up to 10. Even in this unlikely case, the above conclusions remain valid qualitatively. The reasons for such weak winds are investigated with special emphasis on the modified wind momenta: - There may be a break-down of the wind momentum-luminosity relation (WLR) for dwarf stars at low luminosity (log L/L� < 5.5). However, reasons for such a breakdown remain unknown. - The slope of the WLR may be steeper at low metallicity. This is predicted by the radiation driven wind theory, but the current hydrodynamical simulations do not show any change of the slope at SMC metallicity. Moreover, there are indications that some Galactic objects have wind momenta similar to those of the SMC stars. - Decoupling may take place in the atmosphere of the SMC-N81 stars, leading to multicomponent winds. However, various tests indicate that this is not likely to be the case. The origin of the weakness of the wind observed in the SMC-N81 stars remains unknown. We suggest that this weakness may be linked with the youth of these stars and represents possibly the onset of stellar winds in recently formed massive stars.
Estimating a room size using encoders and collision detectors: application to a cleaning mobile robot

Floor cleaning is a typical mobile robot application. Most commercial cleaning mobile robots designed for domestic applications use random path-planning algorithms and very few sensors and the critical problem is to decide when the cleaning is already finished or the coverage completed. In this paper, we propose some methods to estimate the size of the cleaning area using only the information of encoders and collision detectors. To this end, three typical rooms have been tested with and without furniture. Results show the great influence of furniture distribution in the estimation of the area of the room. Average estimation errors under plusmn20% can be expected for empty rooms and from 0% to -70% in the case of rooms with furniture.
The theories of Detection of Multi-Frequency Weak Signal Based on Stochastic Resonance

There are plenty of weak audio analog signals in ocean, whose signal characteristic is usually an important basis for the target detection in ocean, therefore it is necessary to seek for approaches to detect the weak audio signal. Stochastic resonance, which is regarded as a novel method for detecting the weak signal, has been applied in many areas. However, the most researches concentrate on the single-frequency weak signal, and less research on the multi-frequency weak signal. Starting from the basic bistable stochastic resonance system, theoretical analysis on the multi-frequency analog signal with strong noise is carried out, and a method for detecting and recovering the multi-frequency analog signal is proposed, which is based on the average inversion and interpolation fitting. The analysis and simulation results present that, the multi-frequency analog signal is successfully detected and recovered in the condition that the input signal-to-noise ratio (SNR) is -22dB.Compared with the traditional filter method, the proposed method can effectively reduce the waveform distortion and phase delay of the multi-frequency signal, and it is especially suitable for the detection of multi-frequency weak signals whose frequencies are very close, which makes it possess good theoretical guidance and practical application value.
Mutation-based test-case prioritization in software evolution

During software evolution, to assure the software quality, test cases for an early version tend to be reused by its latter versions. As a large number of test cases may aggregate during software evolution, it becomes necessary to schedule the execution order of test cases so that the faults in the latter version may be detected as early as possible, which is test-case prioritization in software evolution. In this paper, we proposed a novel test-case prioritization approach for software evolution, which first uses mutation faults on the difference between the early version and the latter version to simulate real faults occurred in software evolution, and then schedules the execution order of test cases based on their fault-detection capability, which is defined based on mutation faults. In particular, we present two models on calculating fault-detection capability, which are statistics-based model and probability-based model. Moreover, we conducted an experimental study and found that our approach with the statistics-based model outperforms our approach with the probability-based model and the total statement coverage-based approach, and slightly outperforms the additional statement-coverage based approach in many cases. Furthermore, compared with the total or additional statement coverage-based approach, our approach with either the statistics-based model or the probability-based model tends to be stably effective when the difference on the source code between the early version and the latter version is non-trivial.
Is Public-Private Partnership Obsolete ? Assessing the Obstacles and Shortcomings of PPP Claude Ménard

Public-Private Partnership has been high on the age nda of public decision makers since the 1990’s. Primarily a contractual approach to the del ivery of infrastructures, goods and services traditionally provided by the public sector or by p rivate operators submitted to tight regulation, PPP is also a very special contractual practice as it seeks to introduce market-type relationships in a context in which non-market forc es play a major role. An important consequence is the overlapping of decision rights a s well as property rights, which exposes PPP to a double alignment problem, organizational a nd institutional. Away from the ideological controversies about the legitimacy of P PP in provisioning public goods, this chapter focuses on problems rooted in the very natu re of PPPs and the actual design of their supportive contracts, as well as in the institution s in which they are embedded and that define the capacity to implement and monitor these arrange ments properly. 1 Previous versions of this paper were presented at conferences and seminars in Delft (TU), Lausanne (EPFL), Singapore (NUS), Paris (Sorbonne). I am indebted to participants for their comments and to the co-editors (Piet de Vries and Etienne Yehoue) for their help and supportive suggestions. Nicholas Sowels helped polishing the paper. The usual disclaimer fully applies.
An RSSI-based error collection applied to estimated sensor locations

We consider a wireless sensor network (WSN) that needs to install hundreds of sensor nodes over a vast extent of land, (e.g., an orchard temperature sensor network). For monitoring critical events such as frost-prone temperature over an orchard, WSN users, (namely farmers) need to identify all sensor locations, which is however overwhelming from the viewpoints of costs and man power. To address this problem, we enabled non-GPS-adapted sensors to accurately estimate their locations by applying our RSSI (received signal strength indication)-based error-correcting algorithm to a conventional location-estimating method named Gomashio [1]. The paper presents our algorithm and demonstrates its performance improvement in estimating sensor locations as compared to Gomashio.
The unbalancing effect of hubs on K-medoids clustering in high-dimensional spaces

Unbalanced cluster solutions are affected by very different cluster sizes, with some clusters being very large while others contain almost no data. We demonstrate that this phenomenon is connected to `hubness', a recently discovered general problem of machine learning in high dimensional data spaces. Hub objects have a small distance to an exceptionally large number of data points, and anti-hubs are far from all other data points. In an empirical study of K-medoids clustering we show that hubness gives rise to very unbalanced cluster sizes resulting in impaired internal and external evaluation indices. We compare three methods which reduce hubness in the distance spaces and show that with the balancing of the clusters evaluation indices improve. This is done using artificial and real data sets from diverse domains.
Virtual Backbone of Connected Dominating Set in Wireless Ad Hoc Networks

Wireless ad hoc networks can be flexibly and quickly deployed for many applications such as automated battlefield, search and rescue, and disaster relief. Unlike wired networks or cellular networks, no physical backbone infrastructure is installed in wireless ad hoc networks. A communication session is achieved either through a single-hop radio transmission if the communication parties are close enough, or through relaying by intermediate nodes otherwise. In this chapter, we assume that all nodes in a wireless ad hoc network are distributed in a two-dimensional plane and have an equal maximum transmission range of one unit. The topology of such wireless ad hoc network can be modeled as a unit-disk graph [6], a geometric graph in which there is an edge between two nodes if and only if their distance is at most one .
Learned saliency transformations for gaze guidance

The saliency of an image or video region indicates how likely it is that the viewer of the image or video fixates that region due to its conspicuity. An intriguing question is how we can change the video region to make it more or less salient. Here, we address this problem by using a machine learning framework to learn from a large set of eye movements collected on real-world dynamic scenes how to alter the saliency level of the video locally. We derive saliency transformation rules by performing spatio-temporal contrast manipulations (on a spatio-temporal Laplacian pyramid) on the particular video region. Our goal is to improve visual communication by designing gaze-contingent interactive displays that change, in real time, the saliency distribution of the scene.
Does the Policy Lending of the Government Financial Institution Substitute for the Private Lending during the Period of the Credit Crunch? Evidence from loan level data in Japan

Using the data of individual loan contracts extended by the Japan Finance Corporation for Small and Medium Enterprise (JASME), which is one of the predecessor institutions of the Japan Finance Corporation (JFC) that aimed at lending to small and medium enterprises (SMEs), we examine whether the JASME's lending substituted for the reduced lending supply by private banks during the period of the credit crunch. We find that the JASME made larger loans to the firms whose main bank reduced more lending due to losses on their capital.
An iterative non-parametric approach to the estimation of polyspectra

A number of powerful tools for analyzing linear and nonlinear data sets are based on various spectral measures. In particular, the bispectrum is commonly used for testing Gaussianity and linearity. Due to their inherent robustness to model assumptions, non-parametric estimators of the polyspectra are of particular importance. Unfortunately, the most commonly used non-parametric estimator, the windowed-periodogram, suffers from large sidelobes and fails to provide high-resolution estimates. In this paper, we develop a non-parametric estimator that utilizes the recently introduced iterative adaptive approach (IAA) to provide high-resolution estimates of the polyspectra for nonlinear data. Using the IAA method, we first obtain estimates of the spectral amplitudes and the covariance matrix iteratively, and then use the spectral amplitudes to form accurate estimates of the polyspectra. The developed estimator can be extended to the application of unevenly sampled data, and can also be used in the statistically efficient estimation of coherence polyspectra. The effectiveness of the proposed estimator is demonstrated with both real and simulated data.
Wavelet-based Remote Sensing Image Fusion with PCA and Feature Product

Image fusion is one of the important techniques for image information enhancing. In order to utilize respective information from different remote sensing images, we propose a new image fusion method based on the principal component analysis (PCA) and feature product of wavelet transform. Firstly, the multi-spectral image is transformed with PCA. Secondly, the histogram-matched panchromatic image and the first principal component are decomposed into wavelet coefficients respectively. Thirdly, the first principal component of the multi-spectral image and the panchromatic image are merged with feature product of wavelet based fusion method, and the former is replaced with the merged data. Finally, the new multi-spectral image is obtained by inverse PCA. Some evaluation parameters are suggested and applied to compare the new method with those of PCA method, the combined PCA and traditional wavelet method and the combined PCA and local deviation of wavelet method. Subjective visual effect and objective statistical results indicate that the performance of the new method is better than those methods. It not only preserves spectral information of the original multi-spectral image well, but also enhances spatial detail information greatly
DIP: Disruption-Tolerance for IP

Disruption Tolerant Networks (DTN) have been a popular subject of recent research and development. These networks are characterized by frequent, lengthy outages and a lack of contemporaneous end-to-end paths. In this work we discuss techniques for extending IP to operate more effectively in DTN scenarios. Our scheme, Disruption Tolerant IP (DIP) uses existing IP packet headers, uses the existing socket API for applications, is compatible with IPsec, and uses familiar Policy-Based Routing techniques for network management.