Miracle-dz/UniFAR-FTUs huggingface dataset:
Regularities in the spectrum of chaotic p-modes in rapidly rotating stars

Context. Interpreting the oscillations of massive and intermediate mass stars remains a challenging task. In fast rotators, the oscillation spectrum of p-modes is a superposition of sub-spectra which corresponds to different types of modes, among which island modes and chaotic modes are expected to be the most visible. This paper is focused on chaotic modes, which have not been thoroughly studied before. Aims. We study the properties of high frequency chaotic p-modes in a polytropic model. Unexpected peaks appear in the frequency autocorrelations of the spectra. Our goal is to find a physical interpretation for these peaks and also to provide an overview of the mode properties. Methods. We used the 2D oscillation code “TOP” to produce the modes and acoustic ray simulations to explore the wave properties in the asymptotic regime. Using the tools developed in the field of quantum chaos (or wave chaos), we derived an expression for the frequency autocorrelation involving the travel time of acoustic rays. Results. Chaotic mode spectra were previously thought to be irregular, that is, described only through their statistical properties. Our analysis shows the existence, in chaotic mode spectra, of a pseudo large separation. This means that chaotic modes are organized in series, such that the modes in each series follow a nearly regular frequency spacing. The pseudo large separation of chaotic modes is very close to the large separation of island modes. Its value is related to the sound speed averaged over the meridional plane of the star. In addition to the pseudo large separation, other correlations appear in the numerically calculated spectra. We explain their origin by the trapping of acoustic rays near the stable islands.
On the role of supermassive black holes in quenching star formation in local central galaxies

We analyse the role of AGN feedback in quenching star formation for massive, central galaxies in the local Universe. In particular, we compare the prediction of two semi-analytic models (l-galaxies and sage) featuring different schemes for AGN feedback, with the SDSS DR7 taking advantage of a novel technique for identifying central galaxies in an observational data set. This enables us to study the correlation between the model passive fractions, which is predicted to be suppressed by feedback from an AGN, and the observed passive fractions in an observationally motivated parameter space. While the passive fractions for observed central galaxies show a good correlation with stellar mass and bulge mass, passive fractions in l-galaxies correlate with the halo and black hole mass. For sage, the passive fraction correlate with the bulge mass as well. Among the two models, sage has a smaller scatter in the black hole–bulge mass $({M_{\rm BH}-M_{\rm Bulge}})$ relation and a slope that agrees better with the most recent observations at z ∼ 0. Despite the more realistic prescription of radio-mode feedback in sage, there are still tensions left with the observed passive fractions and the distribution of quenched galaxies. These tensions may be due to the treatment of galaxies living in non-resolved substructures and the resulting higher merger rates that could bring cold gas which is available for star formation.
Measuring the Orbital Parameters of Radial Velocity Systems in Mean-motion Resonance: A Case Study of HD 200964

The presence of mean-motion resonances (MMRs) complicates analysis and fitting of planetary systems that are observed through the radial velocity (RV) technique. MMR can allow planets to remain stable in regions of phase space where strong planet–planet interactions would otherwise destabilize the system. These stable orbits can occupy small phase space volumes, allowing MMRs to strongly constrain system parameters, but making searches for stable orbital parameters challenging. Furthermore, libration of the resonant angle and dynamical interaction between the planets introduces another long-period variation into the observed RV signal, complicating analysis of the periods of the planets in the system. We discuss this phenomenon using the example of HD 200964. By searching through parameter space and numerically integrating each proposed set of planetary parameters to test for long-term stability, we find stable solutions in the 7:5 and 3:2 MMRs in addition to the originally identified 4:3 MMR. The 7:5 configuration provides the best match to the data, while the 3:2 configuration provides the most easily understood formation scenario. In reanalysis of the originally published shorter-baseline data, we find fits in both the 4:3 and 3:2 resonances, but not in the 7:5. Because the time baseline of the data is shorter than the resonant libration period, the current best fit to the data may not reflect the actual resonant configuration. In the absence of a full sample of the longer libration period, we find that it is of paramount importance to incorporate long-term stability when the orbital configuration of the system is fit.
A Tour of Convolutional Networks Guided by Linear Interpreters

Convolutional networks are large linear systems divided into layers and connected by non-linear units. These units are the "articulations" that allow the network to adapt to the input. To understand how a network manages to solve a problem we must look at the articulated decisions in entirety. If we could capture the actions of non-linear units for a particular input, we would be able to replay the whole system back and forth as if it was always linear. It would also reveal the actions of non-linearities because the resulting linear system, a Linear Interpreter, depends on the input image. We introduce a hooking layer, called a LinearScope, which allows us to run the network and the linear interpreter in parallel. Its implementation is simple, flexible and efficient. From here we can make many curious inquiries: how do these linear systems look like? When the rows and columns of the transformation matrix are images, how do they look like? What type of basis do these linear transformations rely on? The answers depend on the problems presented, through which we take a tour to some popular architectures used for classification, super-resolution (SR) and image-to-image translation (I2I). For classification we observe that popular networks use a pixel-wise vote per class strategy and heavily rely on bias parameters. For SR and I2I we find that CNNs use wavelet-type basis similar to the human visual system. For I2I we reveal copy-move and template-creation strategies to generate outputs.
Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks

Hardware-friendly network quantization (e.g., binary/uniform quantization) can efﬁciently accelerate the inference and meanwhile reduce memory consumption of the deep neural networks, which is crucial for model deployment on resource-limited devices like mobile phones. However, due to the discreteness of low-bit quantization, existing quantization methods often face the unstable training process and severe performance degradation. To address this problem, in this paper we propose Differentiable Soft Quantization (DSQ) to bridge the gap between the full-precision and low-bit networks. DSQ can automatically evolve during training to gradually approximate the standard quantization. Owing to its differentiable property, DSQ can help pursue the accurate gradients in backward propagation, and reduce the quantization loss in forward process with an appropriate clipping range. Extensive experiments over several popular network structures show that training low-bit neural networks with DSQ can consistently outperform state-of-the-art quantization methods. Besides, our ﬁrst efﬁcient implementation for deploying 2 to 4-bit DSQ on devices with ARM architecture achieves up to 1.7× speed up, compared with the open-source 8-bit high-performance inference framework NCNN [31].
On-the-fly dense 3D surface reconstruction for geometry-aware augmented reality.

Augmented Reality (AR) is an emerging technology that makes seamless connections between virtual space and the real world by superimposing computer-generated information onto the real-world environment. AR can provide additional information in a more intuitive and natural way than any other information-delivery method that a human has ever in- vented. Camera tracking is the enabling technology for AR and has been well studied for the last few decades. Apart from the tracking problems, sensing and perception of the surrounding environment are also very important and challenging problems. Although there are existing hardware solutions such as Microsoft Kinect and HoloLens that can sense and build the environmental structure, they are either too bulky or too expensive for AR. In this thesis, the challenging real-time dense 3D surface reconstruction technologies are studied and reformulated for the reinvention of basic position-aware AR towards geometry-aware and the outlook of context- aware AR. We initially propose to reconstruct the dense environmental surface using the sparse point from Simultaneous Localisation and Map- ping (SLAM), but this approach is prone to fail in challenging Minimally Invasive Surgery (MIS) scenes such as the presence of deformation and surgical smoke. We subsequently adopt stereo vision with SLAM for more accurate and robust results. With the success of deep learning technology in recent years, we present learning based single image re- construction and achieve the state-of-the-art results. Moreover, we pro- posed context-aware AR, one step further from purely geometry-aware AR towards the high-level conceptual interaction modelling in complex AR environment for enhanced user experience. Finally, a learning-based smoke removal method is proposed to ensure an accurate and robust reconstruction under extreme conditions such as the presence of surgical smoke.
An evaluation of application of information technology and communication of learning science with the theme of solar system

This study aimed to determine (1) the readiness of teacher in applying ICT of science learning, (2) the level according to the application of ICT in the implementation of science learning, (3) the level of completeness of learners after applying ICT in science learning. This evaluation research use the Countenance Stake model that are Antencedent (input), Transaction (process), and Outcomes (output). Respondents involved in this study were Science teachers and students in Junior High School in Yogyakarta that consists of SMPN 5, SMPN 8, SMPN 15, and SMP Pangudi Luhur 1 Yogyakarta.Data collection techniques using purposive sampling using the instrument observation sheets, questionnaires teachers, questionnaires learners interview guidelines and checklist documents RPP. Data were analyzed using a quantitative approach to the t-score, anda qualitative approach descriptively.The results of this study are (1) the preparation of teachers in applying ICT is still not optimal. (2) the suitability of the use of ICT in the learning process quite well, it was shown that on average the suitability of the use of ICT 48,24% at a preliminary stage, 53,21% on core activities. 47,20% at the closing, and 36,74% in the assessment of the range of 56-68%, (3) learning Outcomes are still unsatisfactory ie 62,72% of the normal standard of 70%.
DISCOVERING USER ACCEPTANCE OF BRING YOUR OWN DEVICE(BYOD) IN HIGHER EDUCATION

The purpose of this study is to investigate user acceptance of bring your own device (BYOD) with first year students registered for Crop Science. BYOD refers to the practice of people bringing their own laptops, tablets, smartphones, or other mobile devices with them to learning or work environments. The key aspect of it is that of ownership. Today’s students view mobile devices as a tool that assist them with the mastery of technology skills. When students engage with the studies on mobile devices, it eventually supports them to improve their studies or work on homework and assignments. The participants in this study, were 210 first year students registered at a study university of technology in the Faculty of Science, Department of Agriculture. There were 130 (61.9%) female and 80 (38.1%) males. Their age ranged between 18 and 27 years. The question posed in this study was: How was user acceptance of BYOD discovered? To answer this question, the quantitative method was used. Data were collected by means of the Technology Acceptance Model (TAM) questionnaire. Data were analysed using SPSS. Reliability for the entire scale computed in terms of the internal consistency was found to be . 90. For the content validity, the Kaiser-Meyer-Olkin and Bartlett’s test of sphericity was . 92 which was statistically significant (p < 0.05). The results revealed that 91.9% of the students used smartphones. In terms of the perceived usefulness of BYOD, 81.9% of the students indicated that they were able to use mobile devices to effectively complete the activities on time. It is critical that higher education institutions provide Wi-Fi connection in the classrooms, in order for the students and lecturers to access the Internet during teaching and learning.
PERSONALIZED SCHOLARLY ARTICLE RECOMMENDATIONS BASED ON THE RECURRENT NEURAL NETWORK AND PROBABILISTIC MODELS

Searching scholarly articles, i.e., research papers, on the Web is a challenge even for researchers who are familiar with the searched topics and articles of a particular domain of interest, needless to say for users who are not familiar with the areas of study. Existing research paper recommendation systems either rely on the contentbased or collaborative-filtering approach, or its hybrid model. These recommenders, however, are vulnerable to the cold-start problem, either on new users or new scholarly articles. In this paper, we propose an innovative personalized scholarly article recommendation system which suggests research papers of the same subject area using the recurrent neural network model and ranks closely related research papers using the BM25 probabilistic model. Experimental results based on the titles and abstracts of research papers extracted from ACM DL and IEEE Xplore digital library verify the merit of the proposed recommender, which outperforms recently-developed research paper recommendation systems.
Efficient Balanced and Maximum Homopolymer-Run Restricted Block Codes for DNA-Based Data Storage

We analyze codes for DNA-based data storage, which accounts for the maximum homopolymer repetition length and GC-AT balance. We present a new precoding method for translating words with a maximum run of <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> zeros into words with a maximum homopolymer run <inline-formula> <tex-math notation="LaTeX">$m=k+1$ </tex-math></inline-formula>, which is attractive for securing GC-AT balance. Generating functions are presented for enumerating the number of <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-symbol <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-constrained codewords of given GC-AT balance. Various efficient constructions are presented for block codes that satisfy a combined balance and maximum homopolymer run.
Chronic kidney disease detection using ensemble classi ers and feature set reduction

Chronic kidney disease (CKD) is the gradual loss of kidney function over a duration of months or years. One in ten people are affected by it at some stage. Some ethnicities such as African Americans and South Asians are predisposed to having the disease. Globally the number of people affected has been growing through the years, with 752.7 million having the disease in 2016 The disease has no cure, so early detection is key to better manage the disease and control other risk factors such as diabetes and blood pressure. Although CKD has no early symptoms and requires medical tests on blood and/or urine samples, medical tests conducted for other diseases hold clues to whether someone has CKD . The datasets that are available have a multitude of features and are also incomplete and imbalanced. We want to overcome this problems through feature engineering to reduce the number of features. A comparative study of various classifiers needs to be done to find those that hold promise and are robust enough to handle currently available datasets, which are both incomplete and unbalanced. Our study is to bring down the number of attributes/features using recursive feature elimination method and use Ensemble classifier to predict the existence of CKD from the reduced features.
HiNet: Hybrid Inherited Feature Learning Network for Facial Expression Recognition

In this letter, we propose a novel lightweight network HiNet: hybrid inherited feature learning network and its variants: HiNet -ReLU, HiNet -Concat, Large Scale HiNet, 2 stack HiNet and 4 stack HiNet for facial expression recognition. In HiNet, we introduce the hybrid feature (HyFeat) block that follows a two - level hybrid structure to capture the local contextual information of expressive regions. In level -1, HyFeat block uses two different scaled conv filters to preserve the domain knowledge features for expressive regions. Similarly, level -2 captures the enriched features through refine edge variation of the receptive fields by employing another two filters. Thus, HyFeat block allows network to perpetuate prominent features of facial expressions and enhances the discriminability of the HiNet. Moreover, performance of the proposed HiNet is evaluated by conducting person dependent (PD) and person independent (PI) experiments on four datasets: CK+, MUG, AFEW and OULU respectively. Experimental results on CK+ dataset prove the adequacy of the proposed HiNet over its variants in terms of accuracy and computational complexity. Furthermore, the proposed HiNet uses approximately 5 /16, 1/138, 1/144 and 1/31 times less parameters as compared to the MobileNet, VGG -16, VGG - 19 and ResNet, respectively. The experimental and computational complexity analysis demonstrate the significant performance improvement of the proposed method over the state-of-the-art techniques in terms of recognition rate.
LARPA: A learning automata‐based resource provisioning approach for massively multiplayer online games in cloud environments

Nowadays, with the development of communication systems, massively multiplayer online games (MMOGs) have become very popular. In these games, the players all over the world dynamically interact with each other by sending play actions such as shootings, movements, or chatting in the form of MMOG sessions in real time through a large‐scale distributed environment. Leveraging affordable cloud computing to host such services is a widely investigated issue. It is because the arrival rate of players to the game environment has to make fluctuations, and the players expect services to be always available with an acceptable quality of service (QoS), especially in terms of the response time. Therefore, the dynamic provisioning of resources in order to deal with fluctuating demands due to variability in the arrival rate of players of the MMOG services is highly recommended. In this paper, we propose a learning‐based resource provisioning approach for MMOG services that is based on the combination of the autonomic computing paradigm and learning automata (LA). The remarkable performance of the proposed approach in terms of response time, cost, and allocated virtual machines (VMs) is assessed through simulation and comparison with the existing approaches.
Development of Peer Tutors Learning Media based on Android Application to Improve Learners Independence

This study aims to develop peer tutoring learning media based on Android applications to improve learners' independence. The alternative taken to achieve this goal is to apply peer tutoring learning media based on Android applications with emphasis learners learning independence especially life skills subjects. The development of this learning media is based on the facts and the thought that the learning process implemented tends to be very conventional and has not used an Android-based media. Tutors also have not optimized the potential of learners to share experiences in meeting learning needs. The research method used is research and development with the following steps: (1) a preliminary study to research and collect information and literature to develop of learning media, (2) compiling conceptual learning media to develop the prototypes and (3) conducting limited trials on the learning media. The results showed that the weakness of tutors used of instructional media was due of the limited availability of Android application-based media facilities owned by community learning centre (CLC), namely: (1). 47% tutors have never made an Android application-based learning media that fits to the learner’s characteristics, (2) 80% tutors did not organized learners to use learning media, (3). 47% tutors did not use media that is suitable with the learning objectives. This condition led to the need of the development an Android application-based peer tutoring learning media as a learning model using Mobile Phone at the teaching and learning process. This method is a kind of learning strategy to improving the learning independence activities of learner’s inequality education program at the CLC. Keywords—learning media; peer tutor; Android; learning independence
Fungal isolates influence the quality of Peucedanum praeruptorum Dunn

The symbiotic relationship between beneficial microorganisms and plants plays a vital role in natural and agricultural ecosystems. Although Peucedanum praeruptorum Dunn is widely distributed, its development is greatly limited by early bolting. The reason for early bolting in P. praeruptorum remains poorly characterized. We focus on the plant related microorganisms, including endophytes and rhizosphere microorganisms, by combining the traditional isolation and culture method with metagenomic sequencing technology. We found that the OTUs of endophytes and rhizosphere microorganisms showed a positive correlation in the whole growth stage of P. praeruptorum. Meanwhile, the community diversity of endophytic and rhizosphere fungi showed an opposite change trend, and bacteria showed a similar change trend. Besides, the microbial communities differed during the pre- and post-bolting stages of P. praeruptorum. Beneficial bacterial taxa, such as Pseudomonas and Burkholderia, and fungal taxa, such as Didymella and Fusarium, were abundant in the roots in the pre-bolting stage. Further, a strain belonging to Didymella was obtained by traditional culture and was found to contain praeruptorin A, praeruptorin B, praeruptorin E. In addition, we showed that the fungus could affect its effective components when it was inoculated into P. praeruptorum. This work provided a research reference for the similar biological characteristics of perennial one-time flowering plants, such as Saposhnikovia divaricate, Angelica sinensis and Angelica dahurica.