Miracle-dz/UniFAR-FTUs huggingface dataset:
AN OPTICAL SURVEY OF THE PARTIALLY EMBEDDED YOUNG CLUSTER IN NGC 7129

NGC 7129 is a bright reflection nebula located in the molecular cloud complex near l = ? > 105. °4, b = ? > +9. °9, about 1.15 kpc distant. Embedded within the reflection nebula is a young cluster dominated by a compact grouping of four early-type stars: BD+65°1638 (B3V), BD+65°1637 (B3e), SVS 13 (B5e), and LkHα 234 (B8e). About 80 Hα emission sources brighter than V ∼ ? > 23 are identified in the region, many of which are presumably T Tauri star members of the cluster. We also present deep (V ∼23), optical ( VR C I C ? > ) photometry of a field centered on the reflection nebula and spectral types for more than 130 sources determined from low dispersion, optical spectroscopy. The narrow pre-main sequence evident in the color–magnitude diagram suggests that star formation was rapid and coeval. A median age of about 1.8 Myr is inferred for the Hα and literature-identified X-ray emission sources having established spectral types, using pre-main sequence evolutionary models. Our interpretation of the structure of the molecular cloud and the distribution of young stellar objects is that BD+65°1638 is primarily responsible for evacuating the blister-like cavity within the molecular cloud. LkHα 234 and several embedded sources evident in near-infrared adaptive optics imaging have formed recently within the ridge of compressed molecular gas. The compact cluster of low-mass stars formed concurrently with the early-type members, concentrated within a central radius of ∼0.7 pc. Star formation is simultaneously occurring in a semi-circular arc some ∼3 pc in radius that outlines remaining dense regions of molecular gas. High dispersion, optical spectra are presented for BD+65°1638, BD+65°1637, SVS 13, LkHα 234, and V350 Cep. These spectra are discussed in the context of the circumstellar environments inferred for these stars.
Flavonoids extracted from orange peelings tangelo (Citrus reticulata x Citrus paradisi) and their application as a natural antioxidant in sacha inchi (Plukenetia volubilis) vegetable oil

The purpose of this research was to obtain extracts of flavonoids from orange tangelo peelings (Citrus reticulata x Citrus paradisi) and apply them as natural antioxidant to the vegetable oil Sacha inchi (Plukenetia volubilis). The information was obtained from experimental tests which were made in seven steps: physical and chemical characterization of the orange tangelo and the sacha inchi oil; extraction of flavonoids by the Soxhlet extractor; detection of flavonoids in each extract through fine layer chromatography and paper chromatography; identification and quantification of flavonoids by high resolution chromatography liquid (HPLC); determination of the antioxidant capacity of each extract of flavonoid, and finally evaluation of the antioxidant activity in oil sacha inchi, of the extract with the higher level of antioxidant capacity using a multifactorial design with two factors: extracts of flavonoids and concentration at three levels: 0.05%; 0.1% and 0.15%, and 16 blocks which are the times. The results showed that the liquid extract had the greatest quantity of flavonoids: 100.3724 mg/g, being naringine with 81.1727 mg/g, the flavonoid found in greatest proportion. At a concentration of 0.1%, the extract of flavonoid showed the lowest index of peroxide at 384 hours. It is concluded that the flavonoids present in the orange tangelo peeling can be used as raw extracts, without needing partial or total purification, to increase the sacha inchi oil life.
Vehicle recognition based on Haar features and Adaboost cascade classifier

With the progress of world science and economic development, more and more industries are developing towards intelligence and automation. In the field of intelligent driving, the intelligent vehicle environment perception method based on machine vision has become a hot research topic. Based on monocular vision system, aiming at the requirements of different target features and detection accuracy and efficiency, this paper improves the Haar feature and Adaboost cascade classifier recognition algorithm combined with gray symmetry method to adapt to the recognition environment required by vehicles. The measured results show that the improved vehicle identification method combined with the tracking method based on Kalman filter can reduce the misjudgment rate of vehicles and has good real-time performance.
Compressed Sensing Reconstruction Based on Residual Block and Dense Block

Existing compressed sensing image reconstruction algorithms have a large amount of computation and poor reconstruction quality. In order to solve the above problems, this paper proposes an image compressed sensing reconstruction network (RD-Net) based on residual blocks and dense blocks. The sampling network divides the input image into blocks with a block size of and uses convolution checks. These image blocks are compressed and sampled to obtain compressed sensing measurements. The initial reconstruction network uses Reshape+Concat to generate the initial reconstructed image. The deep reconstruction network is composed of residual blocks and dense blocks, which improves the quality of reconstructed images and also improves the running speed. Experimental results show that RD-Net improves the quality of reconstructed images.
Pedestrian re-recognition method based on dual attention and GRU

In view of the existing problems of pedestrian re-recognition in real scenes, such as camera Angle change, pedestrian posture change and object occlusion, in order to improve the accuracy of pedestrian re-recognition and enhance the ability of pedestrian feature representation, a pedestrian re-recognition method combining double attention and gated cycle unit (GRU) was established based on ResNet-50. The global structure information of pedestrian images can be better captured by channel attention and spatial attention, and the temporal information of pedestrian images can be obtained by GRU. Combined with the triad loss training network of difficult sample sampling and the cross-entropy loss training network based on label smoothing, the experiments were carried out on dataset Market1501 and CUHK03, and the accuracy of rank-1 reached 96.2% and 76.7%, respectively, which increased by 2% and 2.9% compared with the original ResNet-50 backbone network. Experimental results show that the proposed model can effectively improve the accuracy of pedestrian re-recognition.
Research on Named Entity Recognition Based on Model Fusion

In view of the defects of using deep learning method to identify named entities, which can’t represent polysemy and doesn’t contain context information, this paper proposes a new fusion model, which organically combines the bidirectional gated recurring neural network (BiGRU) and the dynamic graph edge evolutionary network (DGCNN) which are improved with attention mechanism. The time sequence features of the vector are captured by the improved BiGRU-ATT, and the local spatial features of the vector are enriched by DGCNN. Finally, fused output results are corrected and output by the constraint rules of CRF model, and the global optimal sequence is obtained by calculating the probability. The experimental results demonstrate that the proposed model is improved compared with the single model, which further highlights the superiority of the proposed fusion model.
The effects of dilution gas on nanoparticle growth in atmospheric-pressure acetylene microdischarges

A two-dimensional multi-fluid model is developed to investigate the effects of dilution gas on microplasma properties and nanoparticle behavior in atmospheric-pressure radio-frequency acetylene discharges. The percentage of dilution gases (argon and helium) percentage varied from 0% to 90%, with the pressure kept constant. Simulation results show that the dilution gas percentage has a significant influence on the spatial distributions of the electron density and temperature, as well as on the formation of nanoparticles in acetylene microplasmas. With increasing dilution gas percentage, the electron density profile changes continuously from being high at the edge to high in the center. A mode transition from a mixed discharge mode with both α regime and drift-ambipolar regime into α regime occurs, which is associated with a sudden decrease in the electron density of the presheaths and an increase in the electron temperature of the bulk plasma. The mode transition point corresponds to the lowest number density ratio of hydrocarbon ions to acetylene. The highest number density ratio is observed at a dilution percentage of 90%, and causes more effective nucleation and coagulation of nanoparticles. Furthermore, owing to the high ionization potential of helium, the transition point moves to a larger dilution gas percentage in C2H2 /He microplasmas. Finally, the growth of nanoparticles via coagulation is studied.
Gas Concentration Prediction Method Based on Denoising Deep Forest

Response lag is an inevitable problem for most electrochemical gas sensors, while the information about gas concentration and trends should be obtained immediately in some specific fields. Therefore, focusing on the goal of improving the accuracy and time efficiency of gas concentration prediction, and adopting data-driven method, this paper proposes a novel regression prediction model—Denoising Deep Forest(DDF). Comparative experiment’s results show that Denoising Deep Forest’s MAPE value is 4.43% and R2 value is 0.991 on ethylene oxide gas leakage detection dataset, which outperforms other common models. Moreover, this paper designs a noise-influence experiment to further test the performance of the models. The results of noise-influence experiment indecate that compared with the common models, Denoising Deep Forest has stronger anti-noise ability and it can maintain the robust performance on data with various noises.
Research on reasoning method of multi-relational knowledge graph based on translation

In the knowledge reasoning filed, algorithms based on translation have achieved great results in recent years. However, when translation-based models fit multi-map attribute relations, different relations will compete for the same embedding vector. At the same time, many knowledge graphs can automatically extract new triples from the Internet now. This further reduces the frequency of occurrence of a single relation in the knowledge graph, which creates more difficulty for translation-based models. Thus, the TransE_rel model is proposed by relaxing the constraints on relation vectors in the low-dimensional space. This model focuses more on relation reasoning and, when combined with other models, can also be used for entity reasoning. We conduct relation prediction and entity prediction on benchmark dataset and one dataset extracted by ourselves. By comparing with recent work, experimental results show that our method has achieved better performance on two tasks especially on relation prediction.
Apply Yolov4-Tiny on an FPGA-Based Accelerator of Convolutional Neural Network for Object Detection

With the continuous expansion of Neural Network technology in the artificial intelligence field, for example, image recognition and retrieval, object detection, pixel processing, automatic speech generation, etc., Convolutional Neural Networks (CNN) and Deep Learning of Neural Networks (DNN) have made apparent breakthroughs. To improve the inference speed of images, the combination of FPGA-based acceleration and multiple model quantization methods has become one of the most contemporary alternative methods. This paper designed an FPGA-Based acceleration scheme combining software and hardware and effectively applied it to the Yolov4-Tiny object detection model, realizing the accelerated detection inference process from the original 6-7mins to 383ms. First, it chose the static quantization method of fixed-point numbers, fixed the position of the decimal point, and then added Batch Norm between the convolutional layer and the activation function to form a connection structure. Second, it further improved inference speed on an FPGA with a version of ZYNQ-7020 by increasing the bandwidth cap and reducing bandwidth requirements, employing a massive pipeline design. Finally, in the test of the Coco dataset, the plan has completed a substantial acceleration of the average inference speed of the Yolov4-Tiny object detection model from 7.13mins/Picture to 498.89ms/Picture, which has a high application value in the field of object detection. It dramatically improves the inference speed as well as keeps the average accuracy above 0.95.
Research Status and Development Trend of Image Restoration Technology

Defocus of the imaging system, relative motion between the equipment and the object or inherent defects of the equipment will lead to the degradation of image quality. Usually, image restoration is required before image processing, and the results of restoration technology affect the effect of image processing. In order to study the effect of image restoration technology, the image restoration technology was systematically sorted out. According to the different restoration models, the image restoration technology is divided into the method based on regularization and the method based on Kalman filter, and the two methods are summarized and explained respectively. By analyzing the restoration models established by the two methods and their solving process, it is found that the method based on regularization has advantages in retaining image information, and the method based on Kalman filter has advantages in terms of speed. The development trend of image restoration technology is analyzed from the summary of the two restoration methods, and suggestions and thoughts are put forward for the development of image restoration technology. In the future, image restoration technology should make use of deep learning algorithm, combined with the actual environment and industry characteristics, to achieve intelligent, practical and domain.
Journal Pre-proof Does

Sleep stabilizes newly acquired memories, a process referred to as memory consolidation. According to recent studies, sleep-dependent consolidation processes might be deployed to different extents for different types of memories. In particular, weaker memories might benefit greater from post-learning sleep than stronger memories. However, under standard testing conditions, sleep-dependent consolidation effects for stronger memories might be obscured by ceiling effects. To test this possibility, we devised a new memory paradigm (Memory Arena) in which participants learned temporospatial arrangements of objects. Prior to a delay period spent either awake or asleep, training thresholds were controlled to yield relatively weak or relatively strong memories. After the delay period, retrieval difficulty was controlled via the presence or absence of a retroactive interference task. Under standard testing conditions (no interference), a sleep-dependent consolidation effect was indeed observed for weaker memories only. Critically though, with increased retrieval demands, sleep-dependent consolidation effects were seen for both weaker and stronger memories. These results suggest that all memories are consolidated during sleep, but that memories of different strengths require different testing conditions to unveil their benefit from post-learning sleep.
Gauged Supergravities from Spherical Reductions

This dissertation is devoted to deriving the bosonic sectors of certain gauged supergravities in various dimensions from reducing eleven-dimensional supergravity, type IIA and type IIB supergravities in ten dimensions on certain spherical spaces. Explicit non-linear Kaluza-Klein ans\"atze for reductions of eleven-dimensional supergravity and of type IIA and type IIB supergravities on $S^n$ and $S^n\times T^m$ are presented. Knowing explicit non-linear ans\"atze is proven to be very useful in finding super Yang-Mills operators of gauge theories via AdS/CFT correspondence. We present a sample calculation which allows us to find a super Yang-Mills operator using a non-linear ansatz. Knowing non-linear ans\"atze is also useful for finding supergravity duals to certain twisted supersymmetric gauge theories. These supergravity solutions are branes wrapped on certain supersymmetric cycles. Some solutions, which are dual to gauge theories in three and five dimensions, are presented.
S OCIO -E CONOMIC A SPECTS OF S PORTS P ARTICIPATION FOR G IRL S TUDENTS OF M IDDLE -I NCOME G ROUP

A family’s social and economic standing in society mayinfluences the participation ofgirls in sports at school and college levels. The objective of the present study is to assess the impact of social and economic aspects of middle-income groups on girls’ sports participation and perception in higher education i.e. family income, education of parent, and occupation of parent in Lucknow district. The study was based on primary data sources collected through a questionnaire-based survey filled by 325 girl students of the middle-income group. The study demonstrated a high prevalence of socioeconomic barriers among non-participating female students. The non- professional mother’s low education was significantly related to lack of confidence and peer pressure among girls. Similarly, non-professional fathers with low education were related significantly to barriers and focused on educational achievement. For Sports participantsgirls, non-professional parent was correlated with a lack of interest in sport, health issues, and transportation facilities constraints. The study highlighted the need for measures to improve girls’ participation in sports by reducing the perception of socio-economic barriers.
Back-Pressure Based Routing

Back-Pressure routing (BPR) [3] aims to optimize network throughput by considering the occupancy of network interface queues. Reducing the queue backlog often comes with a lower packet delay and an overall increased throughput. One drawback of BPR is, that only considering queue occupancy values can lead to unnecessary long paths and therefore increased resource use leading to lower throughput. Different approaches exist that address this drawback such as [4]. Motivation
geneXtendeR: optimized functional annotation of ChIP-seq data

Motivation Different ChIP-seq peak callers often produce different output results from the same input. Since different peak callers are known to produce differentially enriched peaks with a large variance in peak length distribution and total peak count, accurately annotating peak lists with their nearest genes can be an arduous process. Functional genomic annotation of histone modification ChIP-seq data can be a particularly challenging task, as chromatin marks that have inherently broad peaks with a diffuse range of signal enrichment (e.g., H3K9me1, H3K27me3) differ significantly from narrow peaks that exhibit a compact and localized enrichment pattern (e.g., H3K4me3, H3K9ac). In addition, varying degrees of tissue-dependent broadness of an epigenetic mark can make it difficult to accurately and reliably link sequencing data to biological function. Thus, it would be useful to develop a software program that can precisely tailor the computational analysis of a ChIP-seq dataset to the specific peak coordinates of the data. Results geneXtendeR is an R/Bioconductor package that optimizes the functional annotation of ChIP-seq peaks using fast iterative peak-coordinate/GTF alignment algorithms focused on cis-regulatory regions and proximal-promoter regions of nearest genes. The goal of geneXtendeR is to robustly link differentially enriched peaks with their respective genes, thereby aiding experimental follow-up and validation in designing primers for a set of prospective gene candidates during qPCR. We have tested geneXtendeR on 547 human transcription factor ChIP-seq ENCODE datasets and 214 human histone modification ChIP-seq ENCODE datasets, providing the analysis results as case studies. Availability The geneXtendeR R/Bioconductor package (including detailed introductory vignettes) is available under the GPL-3 Open Source license and is freely available to download from Bioconductor at: https://bioconductor.org/packages/devel/geneXtendeR/. Contact bohdan@stanford.edu Supplementary information Supplementary data are available at Bioinformatics online.
Σ 1 ( κ ) -DEFINABLE SUBSETS OF H( κ + )

We study Σ 1 ( ω 1 )-deﬁnable sets (i.e. sets that are equal to the collection of all sets satisfying a certain Σ 1 -formula with parameter ω 1 ) in the presence of large cardinals. Our results show that the existence of a Woodin cardinal and a measurable cardinal above it imply that no well-ordering of the reals is Σ 1 ( ω 1 )-deﬁnable, the set of all stationary subsets of ω 1 is not Σ 1 ( ω 1 )- deﬁnable and the complement of every Σ 1 ( ω 1 )-deﬁnable Bernstein subset of ω 1 ω 1 is not Σ 1 ( ω 1 )-deﬁnable. In contrast, we show that the existence of a Woodin cardinal is compatible with the existence of a Σ 1 ( ω 1 )-deﬁnable well-ordering of H( ω 2 ) and the existence of a ∆ 1 ( ω 1 )-deﬁnable Bernstein subset of ω 1 ω 1 . We also show that, if there are inﬁnitely many Woodin cardinals and a measurable cardinal above them, then there is no Σ 1 ( ω 1 )-deﬁnable uni- formization of the club ﬁlter on ω 1 . Moreover, we prove a perfect set theorem for Σ 1 ( ω 1 )-deﬁnable subsets of ω 1 ω 1 , assuming that there is a measurable car- dinal and the non-stationary ideal on ω 1 is saturated. The proofs of these results use iterated generic ultrapowers and Woodin’s P max -forcing. Finally, we also prove variants of some of these results for Σ 1 ( κ )-deﬁnable subsets of κ κ , in the case where κ itself has certain large cardinal properties.
Flow characteristics in a crowded transport model

The aim of this paper is to discuss the appropriate modelling of in- and outflow boundary conditions for nonlinear drift-diffusion models for the transport of particles including size exclusion and their effect on the behaviour of solutions. We use a derivation from a microscopic asymmetric exclusion process and its extension to particles entering or leaving on the boundaries. This leads to specific Robin-type boundary conditions for inflow and outflow, respectively. For the stationary equation we prove the existence of solutions in a suitable set-up. Moreover, we investigate the flow characteristics for a small diffusion parameter ε, which yields the occurrence of a maximal current phase in addition to well-known one-sided boundary layer effects for linear drift-diffusion problems. In a 1D set-up we provide rigorous estimates in terms of ε, which confirm three different phases. Finally, we derive a numerical approach to solve the problem also in multiple dimensions.
Negative refractive perfect lens vs Spherical geodesic lens. Perfect Imaging comparative analysis

Negative Refractive Lens (NRL) has shown that an optical system can produce images with details below the classic Abbe diffraction limit. This optical system transmits the electric field, emitted by the object surface, towards the image surface producing the same field distribution in both surfaces. In particular, a Dirac delta electric field in the object surface is focused without diffraction limit to the Dirac delta electric field in the image surface. The Maxwell Fish Eye lens (MFE) and the Spherical Geodesic Waveguide (SGW) have been claimed to break the diffraction limit using positive refraction with a different meaning. In these cases, it has been considered the power transmission from a point source to a point receptor, which falls drastically when the receptor is displaced from the focus by a distance much smaller than the wavelength. Although these systems can detect displacements up to Wavelength/500, they cannot be compared to the NRL, since the concept of the object and image surface is not established. Here, it is presented an analysis of the SGW with defined object and image surfaces. The results show that a Dirac delta electric field on the object surface produces an image below the refraction limits on the image surface.