Miracle-dz/UniFAR-FTUs huggingface dataset:
Inviscid protostellar disc ring formation and high-density ring edges due to the ejection and subsequent infall of material onto a protostellar disc

Abstract Discs of gas and dust are ubiquitous around protostars. Hypothetical viscous interactions within the disc are thought to cause the gas and dust to accrete onto the star. Turbulence within the disc is theorised to be the source of this disc viscosity. However, observed protostellar disc turbulence often appears to be small and not always conducive to disc accretion. In addition, theories for disc and planet evolution have difficulty in explaining the observed disc rings/gaps which form much earlier than expected. Protostellar accretion discs are observed to contain significant quantities of dust and pebbles. Observations also show that some of this material is ejected from near the protostar, where it travels to the outer regions of the disc. Such solid infalling material has a relatively small amount of angular momentum compared to the material in the disc. This infalling material lowers the angular momentum of the disc and should drive a radial flow towards the protostar. We show that the local radial accretion speed of the disc is proportional to the mass rate of infalling material onto the disc. Higher rates of infall onto the disc implies higher radial accretion disc speeds. As such, regions with high rates of infall of gas, dust, and pebbles onto the disc will produce gaps on relatively short timescales in the disc, while regions associated with relative low rates of infalling material will produce disc rings. As such, the inner edge of a disc gap will tend to have a higher surface density, which may enhance the probability of planet formation. In addition, the outer edge of a disc gap will act as a dust trap and may also become a site for planet formation. For the early Solar System, such a process may have collected O $^{16}$ -poor forsterite dust from the inner regions of the protosolar disc and O $^{16}$ -rich CAIs and AOAs from the inner edge regions of the protosolar disc, thereby constructing a region favourable to the formation of pre-chondritic planetesimals.
Sediment Bacteria in the Alpine Lake Sayram: Vertical Patterns in Community Composition

Bacterial communities inhabiting alpine lakes are essential to our understanding of ecosystem processes in a changing climate, but little has been reported about the vertical patterns of sediment bacterial communities in alpine lakes. To address this knowledge gap, we collected the 100 cm long sediment core from the center of Lake Sayram, the largest alpine lake in Xinjiang Uygur autonomous area, China, and used 16S rRNA gene-targeted amplicon sequencing to examine the bacterial populations. The results showed that bacterial diversity, as estimated by the Shannon index, was highest at the surface (6.9849 at 0–4 cm) and gradually decreased with depth up to 3.9983 at 68–72 cm, and then increased to 5.0927 at 96–100 cm. A total of 56 different phyla and 1204 distinct genera were observed in the sediment core of Lake Sayram. The bacterial community structure in the sediment samples from the various layers was dissimilar. The most abundant phyla in alpine Lake Sayram were Proteobacteria, Firmicutes, and Planctomycetes, accounting for 73%, 6%, and 4% of the total reads, respectively; the most abundant genera were Acinetobacter, Hydrogenophaga, and Pseudomonas, accounting for 18%, 12%, and 8% of the total reads, respectively. Furthermore, the relative abundance of Acinetobacter increased with sediment depth, while the relative abundance of Hydrogenophaga and Pseudomonas decreased with sediment depth. Our findings indicated that the nitrate-reducing bacteria (Acinetobacter, Hydrogenophaga, and Pseudomonas) may be prevalent in the sediment core of Lake Sayram. Canonical correspondence analysis showed that carbonate and total organic carbon (TOC) may be the main environmental factors affecting the vertical patterns of bacterial community composition (BCC) in the sediment of Lake Sayram. This work significantly contributes to our understanding of the BCC of sediments from alpine lakes in arid and semiarid regions.
A Charge Prediction Model Based on Category Frequency Division

Charge prediction aims to charge a defendant through the case description. A fusion model to predict charges based on category frequency partitioning is proposed to address the serious imbalance of categories in charge prediction and the limited capture of text semantic features by existing models. A sample of CAIL2018(China AI and Law Challenge) was classified as low, medium-low, medium, and high frequency based on the frequency of the crime category, and a weighted stratified sampling method was adopted to construct three datasets. Dynamic vectors characterizing the semantics of the text using the pretrained RoBERTa model were used for each of the three datasets to capture the global contextual information. TextCNN, a convolutional neural network with a multi-window sliding mechanism, was used to extract the local semantic features of the text. Additionally, BiGRU, a bidirectional gated recurrent unit network, was used at the same time to capture the information of the textual sequences, combined with Attention to highlight the textual keyword information. The learning of feature representations for the three datasets was completed through multi-channel feature extraction. The outputs of the three parts were further fused by a feedforward neural network to complete the charge prediction. In experiments, the accuracy of the proposed model was 91.65%; the accuracy and Macro_F1 values were improved by 6.84% and 34.88%, respectively, compared with a BERT baseline; and the Macro_F1 value was improved by 1.49% compared with the suboptimal model. The proposed model improved the prediction accuracy of low-, medium-low-, medium-, and high-frequency offenses by 3.02%, 1.16%, 1.06%, and 0.5%, respectively, compared with the suboptimal model, effectively learning the semantic representations and improving the classification accuracy of low-frequency offenses, and providing references to achieve judicially assisted sentencing.
Low-profile and High-gain Dual-Linearly Polarized Offset Reflector Antenna at W-band

This paper presents a single offset-reflector antenna in a parallel plate waveguide (PPW), which is capable of radiating two orthogonal linear polarizations with high gain at W-band (75GHz-110GHz). The offset reflector is illuminated by an H-plane corrugated feeding horn placed inside the PPW. The dual-linear polarization characteristic is achieved by exploiting the two excitation modes of the over-moded PPW (OPPW), which are quasi-TEM and quasi-TE1 modes. Since the offset-reflector only collimates the beam in one plane, the directivity is further improved by 6dB by flaring the aperture along the orthogonal plane. The antenna shows a peak gain of 26dBi at 90GHz and more than 30% bandwidth for both polarizations with a sidelobe level below – 18dB over the entire operating frequency band.
Modeling of snow and ice melt at ETH Camp (West Greenland): A study of surface albedo

[1] The objective of this paper is to present the validation over Greenland of a thermodynamic snow-ice model that was complemented with the snow metamorphism and albedo parameterizations of the Centre d'Etudes de la Neige (Grenoble, France) in order to make the surface albedo variable and interactive. Special attention is given to the surface albedo since it is the most important parameter in energy exchanges with the atmosphere for snow and ice melt. The development of an integrated (snow, ice, and water) albedo model takes into account the different surface types observed on an ice sheet, and the snow albedo is calculated from the simulated surface snow grains. The validation for a polar site has been done at ETH Camp (West Greenland, 1155 m above sea level) during the 1990 and 1991 summer seasons. Although both ablation seasons differed greatly (in 1990 it showed a negative mass balance while in 1991 it ended with a positive mass balance), a single model configuration was able to provide good results for both 1990 and 1991. These simulations show that the snow metamorphism laws included enable an accurate simulation of the surface albedo and henceforth of the surface mass balance for a polar site, provided that the snow model is correctly forced at its surface level and that meltwater retention, percolation, and drainage are well represented in the snow model. The model results are also compared with two other modeling approaches, and differences between the three snow models are detailed.
Privacy-preserving design of graph neural networks with applications to vertical federated learning

The paradigm of vertical federated learning (VFL), where institutions collaboratively train machine learning models via combining each other's local feature or label information, has achieved great success in applications to financial risk management (FRM). The surging developments of graph representation learning (GRL) have opened up new opportunities for FRM applications under FL via efficiently utilizing the graph-structured data generated from underlying transaction networks. Meanwhile, transaction information is often considered highly sensitive. To prevent data leakage during training, it is critical to develop FL protocols with formal privacy guarantees. In this paper, we present an end-to-end GRL framework in the VFL setting called VESPER, which is built upon a general privatization scheme termed perturbed message passing (PMP) that allows the privatization of many popular graph neural architectures.Based on PMP, we discuss the strengths and weaknesses of specific design choices of concrete graph neural architectures and provide solutions and improvements for both dense and sparse graphs. Extensive empirical evaluations over both public datasets and an industry dataset demonstrate that VESPER is capable of training high-performance GNN models over both sparse and dense graphs under reasonable privacy budgets.
Atmospheric Turbulence Degraded Video Restoration with Recurrent GAN (ATVR-GAN)

Atmospheric turbulence (AT) can change the path and direction of light during video capturing of a target in space due to the random motion of the turbulent medium, a phenomenon that is most noticeable when shooting videos at long ranges, resulting in severe video dynamic distortion and blur. To mitigate geometric distortion and reduce spatially and temporally varying blur, we propose a novel Atmospheric Turbulence Video Restoration Generative Adversarial Network (ATVR-GAN) with a specialized Recurrent Neural Network (RNN) generator, which is trained to predict the scene’s turbulent optical flow (OF) field and utilizes a recurrent structure to catch both spatial and temporal dependencies. The new architecture is trained using a newly combined loss function that counts for the spatiotemporal distortions, specifically tailored to the AT problem. Our network was tested on synthetic and real imaging data and compared against leading algorithms in the field of AT mitigation and image restoration. The proposed method outperformed these methods for both synthetic and real data examined.
Monitoring the Effect of Weed Encroachment on Cattle Behavior in Grazing Systems Using GPS Tracking Collars

Simple Summary Pasture weed encroachment is a major challenge in livestock production based on grazing systems. Amaranthus spinosus L. is an annual weed species with high invasive potential worldwide, and it can affect animal behavior and well-being due to the presence of sharp spines. We used GPS tracking collars to monitor cattle activity and spatial distribution in a grazing system with different levels of weed encroachment. Animals in weed-infested paddocks had reduced resting time and increased grazing time, distance traveled, and rate of travel compared to animals in weed-free paddocks. The spatial distribution of the cattle was greater in weed-free paddocks than in weed-strips or weed-infested areas. Pasture weed encroachment affects cattle behavior and their spatial distribution across the pasture. Increasing animal activity can result in lower animal performance due to greater energy expenditure and impacts on animal welfare. Understanding the impact of pasture weed encroachment on animal behavior is especially important for increasing livestock productivity and sustainability while maintaining animal welfare. Abstract Weed encroachment on grasslands can negatively affect herbage allowance and animal behavior, impacting livestock production. We used low-cost GPS collars fitted to twenty-four Angus crossbred steers to evaluate the effects of different levels of weed encroachment on animal activities and spatial distribution. The experiment was established with a randomized complete block design, with three treatments and four blocks. The treatments were paddocks free of weeds (weed-free), paddocks with weeds established in alternated strips (weed-strips), and paddocks with weeds spread throughout the entire area (weed-infested). Animals in weed-infested paddocks had reduced resting time and increased grazing time, distance traveled, and rate of travel (p < 0.05) compared to animals in weed-free paddocks. The spatial distribution of the animals was consistently greater in weed-free paddocks than in weed-strips or weed-infested areas. The effects of weed encroachment on animal activities were minimized after weed senescence at the end of the growing season. Pasture weed encroachment affected cattle behavior and their spatial distribution across the pasture, potentially impacting animal welfare. Further long-term studies are encouraged to evaluate the impacts of weed encroachment on animal performance and to quantify the effects of behavioral changes on animal energy balance.
Investigation of the Influence of the Geometric Dimensions of Permanent Magnets on the Magnitude of the Back Electromotive Force in Synchronous Machines with Sensorless Control

The current publication examines how the geometric parameters of permanent magnets in electric motors with permanent magnets affect the back electromotive force (EMF). Understanding this reverse EMF is crucial for regulating the motor's dynamics based on magnet properties. Investigating the influence of magnet geometry on the reverse EMF and motor speed is crucial in light of rapid advances in electric drive technologies. With new materials and design approaches emerging, there is a need to better understand how magnet parameters affect motor characteristics. By systematically modifying the magnet's geometric parameters, we aim to identify specific changes that cause variations in the reverse EMF and motor speed. This study is valuable for developing efficient electric drives that meet performance standards. Consequently, our article focuses on exploring the relationship between magnet geometry, reverse EMF, and motor speed. We anticipate that the findings will enhance understanding of how magnet parameters impact electric motor performance and contribute to the development of more efficient drive systems.
Relationship between Problematic Internet Use and Adolescent Mental Health in Saudi Arabia

Background : The Internet has transformed into a versatile digital platform encompassing social interactions, academic research, knowledge acquisition, and entertainment and resulting in healthcare problems among all age groups, especially adolescents. This study sought to explore the prevalence of problematic internet use (PIU) and its effects on adolescents‟ mental well-being in the Kingdom of Saudi Arabia. This descriptive, cross-sectional comparative study involved a convenience sample comprising 154 Saudi Arabian adolescents. Data were collected through an online survey, employing the Problematic Internet Use Questionnaire Short Form-6 (PIUQ-SF-6) and the Revised Mental Health Inventory (MHI-5). A substantial proportion of the sample exhibited problematic internet usage patterns, coupled with a moderate level of mental well-being. Except for gender (female) and academic performance (“fair” evaluation grade) in the preceding year, sociodemographic factors exhibited no significant correlation with PIU scores. Mental health scores had a significant negative correlation with PIU; the obsession, neglect, and control subscales; hours spent on the Internet daily, and daily sleeping hours. Conversely, there was a significant positive correlation between PIU and the days per week and hours per day of internet use, as well as with daily sleeping hours. Finally, daily sleeping hours and days per week of internet use significantly predicted adolescents‟ mental health. To foster adolescent mental well-being, it is imperative to explore this area through continued research efforts. Mitigating excessive online engagement and its associated psychological challenges requires a multilevel approach encompassing individual counseling and family-centered interventions.
Linking hinterland evolution and continental basin sedimentation by using detrital zircon thermochronology: a study of the Khorat Plateau Basin, eastern Thailand

The effectiveness of detrital zircon thermochronology as a means of linking hinterland evolution and continental basin sedimentation studies is assessed by using Mesozoic continental sediments from the poorly understood Khorat Plateau Basin in eastern Thailand. New uranium lead (U‐Pb) and fission‐track (FT) zircon data from the Phu Kradung Formation identify age modes at 141 ± 17 and 210 ± 24 Ma (FT) and 2456 ± 4, 2001 ± 4, 251 ± 3, and 168 ± 2 Ma (U‐Pb), which are closely similar to data from the overlying formations. The FT data record post‐metamorphic cooling, whereas the U‐Pb data record zircon growth events in the hinterland. Comparison is made between detrital zircon U‐Pb data from ancient and modern sources across Southeast Asia. The inherent stability of the zircon U‐Pb system means that 250 Myr of post‐orogenic sedimentary recycling fails to change the regional zircon U‐Pb age signature and this precludes use of the U‐Pb approach alone for providing unique provenance information.
Compact Transitions for Loopback Testing of Packaged Antennas with Waveguide Launchers

In this contribution, different compact transitions are presented, showing how they can be used for testing Antenna-in-Package (AiP) transceivers for 76-81 GHz automotive radar in a robust and reproducible way. The packaged antenna chosen for this work can directly launch guided modes into standard WR10 waveguides and thereby termed as “Launcher-in-Package (LiP)”. The presented concepts showcase a loopback from the TX to the RX channel of a single chip transceiver using planar and non-planar strategies. The former strategy is based on a Printed Circuit Board (PCB) where planar transmission lines like microstrip lines (MSL) and substrate integrated waveguides (SiW) are used to transfer power from TX to RX facilitating a loopback test. The non-planar strategy on the other hand is designed with a multi-layer yet compact metallic waveguide block. The paper exhibits design strategies and their simulative performance, so that one can use such transitions to design the full test concept for a certain LiP transceiver.
FAM+G: A Feature Fusion Module for Defect Detection in Dunhuang Murals

The task of defect detection has always been a popular research direction in computer vision. In this paper, we implement the defect detection study on Dunhuang murals by adapting the U2-net deep learning model, which is simple and lightweight. And it is widely used in the field of image segmentation. During the research process, we propose a new feature aggregation module FAM+G (Global Feature aggregation module) for improving the detection effect of U2-net on Dunhuang mural defects. The module improves the model’s ability to locate and classify defects by fusing coarse semantic information and fine texture features, which makes the model’s detection results more accurate. The whole experiment process utilizes a self-built Dunhuang mural dataset for training and testing, and the test results have good metrics.
Development of fetal hearing

Previous research has revealed that the human fetus responds to sound, but to date there has been little systematic investigation of the development of fetal hearing. The development of fetal behavioural responsiveness to pure tone auditory stimuli (100 Hz, 250 Hz, 500 Hz, 1000 Hz, and 3000 Hz) was examined from 19 to 35 weeks of gestational age. Stimuli were presented by a loudspeaker placed on the maternal abdomen and the fetus's response, a movement, recorded by ultrasound. The fetus responded first to the 500 Hz tone, where the first response was observed at 19 weeks of gestational age. The range of frequencies responded to expanded first downwards to lower frequencies, 100 Hz and 250 Hz, and then upwards to higher frequencies, 1000 Hz and 3000 Hz. At 27 weeks of gestational age, 96% of fetuses responded to the 250 Hz and 500 Hz tones but none responded to the 1000 Hz and 3000 Hz tones. Responsiveness to 1000 Hz and 3000 Hz tones was observed in all fetuses at 33 and 35 weeks of gestational age, respectively. For all frequencies there was a large decrease (20-30 dB) in the intensity level required to elicit a response as the fetus matured. The observed pattern of behavioural responsiveness reflects underlying maturation of the auditory system. The sensitivity of the fetus to sounds in the low frequency range may promote language acquisition and result in increased susceptibility to auditory system damage arising from exposure to intense low frequency sounds.
A Historical Context for Data Streams

Machine learning from data streams is an active and growing research area. Research on learning from streaming data typically makes strict assumptions linked to computational resource constraints, including requirements for stream mining algorithms to inspect each instance not more than once and be ready to give a prediction at any time. Here we review the historical context of data streams research placing the common assumptions used in machine learning over data streams in their historical context.
Stochastic homogenization of nonlinear evolution equations with space-time nonlocality

In this paper we consider the homogenization problem of nonlinear evolution equations with space-time non-locality, the problems are given by Beltritti and Rossi [JMAA, 2017, 455: 1470-1504]. When the integral kernel $J(x,t;y,s)$ is re-scaled in a suitable way and the oscillation coefficient $\nu(x,t;y,s)$ possesses periodic and stationary structure, we show that the solutions $u^{\varepsilon}(x,t)$ to the perturbed equations converge to $u_{0}(x,t)$, the solution of corresponding local nonlinear parabolic equation as scale parameter $\varepsilon\rightarrow 0^{+}$. Then for the nonlocal linear index $p=2$ we give the convergence rate such that $||u^\varepsilon -u_{0}||_{_{L^{2}(\mathbb{R}^{d}\times(0,T))}}\leq C\varepsilon$. Furthermore, we obtain that the normalized difference $\frac{1}{\varepsilon}[u^{\varepsilon}(x,t)-u_{0}(x,t)]-\chi(\frac{x}{\varepsilon}, \frac{t}{\varepsilon^{2}}) \nabla_{x}u_{0}(x,t)$ converges to a solution of an SPDE with additive noise and constant coefficients. Finally, we give some numerical formats for solving non-local space-time homogenization.