Miracle-dz/UniFAR-FTUs huggingface dataset:
Predicting Virtual Gifting Behaviors in Live Streaming Using Danmaku Information

In recent years, live streaming has become a popular leisure activity for many people, and the main income of live streamers comes from virtual gifts sent by the viewers on the online platforms. In live streaming rooms hosted on the online platforms, viewers can send danmaku, i.e., broadcasting messages, in the chat channels, and can purchase and send virtual gifts to the streamers. And, the value of virtual gifts sent by the viewers directly affects the streamers' income. However, only a few studies focused on the connection between the danmaku information and the viewers' gifting behaviors. Different from traditional video reviews, danmaku makes it easier for the viewers to see the comments and responses from other viewers, and thus enables the more convenient communication between the viewers. At the same time, the viewers can be easily influenced by danmaku information sent by other viewers through the live streaming chat channels. It is thus reasonable to assume that the gift sending behaviors are closely related to the danmaku information. This study aims to explore how danmaku data can be used to predict the gifting behaviors of viewers. In this research, we collected over 9.5 million danmaku data from 500 live streaming rooms on Douyu.com platform. Specifically, we proposed a novel model that integrates multiple types of semantic information from danmaku, including sentiment, topics, as well as information on the viewers, and used these information to predict the value of virtual gifts sent by the viewers. The results showed that the gifting behaviors of viewers can be well predicted with the feature variables extracted from the danmaku data.
An Energy-Efficient scheme for industrial wireless sensor network based on hierarchical network structure

Industrial wireless sensor Networks (IWSNs) are widely used in Internet of Things (IoT) scenarios. However, the rapid growth of sensor nodes and the transmission of a large number of redundant data lead to excessive energy consumption and accelerate the death of wireless sensor networks. In order to reduce redundant data transmission and energy consumption in wireless sensor networks, a data refusion scheme based on layered network structure (RF-LNS) is proposed. The whole sensor network is divided into direct transmission layer and cluster layer. Cluster layer uses non-uniform clustering to balance network energy consumption and perform the first data fusion. At the same time, a low redundancy data refusion algorithm is designed to perform the second data fusion in the direct transmission layer. Simulation results show that compared with other protocols, RF-LNS scheme can save the energy of network nodes more effectively, reduce the energy consumption rate, and prolong the life of wireless sensor network.
Fluid Mechanics of Propulsion by Cilia and Flagella

Since the Annual Review of Fluid Mechanics first published a review on microorganism locomotion by Jahn & Votta (1972) considerable progress has been made in the understanding of both the biological and the fluid-mechanical processes involved not only in microorganism locomotion but also in other fluid systems utilizing cilia. Much of this knowledge and research, which has been built on the solid foundation of the pioneering work of Sir James Gray (1928, 1968) and Sir Geoffrey Taylor (1951, 1952a,b), has been reported extensively elsewhere, particularly by Gray (1928, 1968), Sleigh (1962), Lighthill (1975), and Wu, Brokaw & Brennen (1975). The subject is now sufficiently broad that it precludes any exhaustive treatment in these few pages. Rather, we restrict this review primarily to a summary of present understanding of the low-Reynolds-number flows associated with microorganism propulsion and the hydromechanics of ciliary systems. In this introductory section we wish to put such fluid-mechanical studies in biological perspective. Section 2 outlines the present status of low-Reynolds-number slender-body theory, and we discuss the application of this theory to biological systems in the final sections.
Decoding the Role of Extracellular Polymeric Substances in Enhancing Nitrogen Removal from High-Ammonia and Low-C/N Wastewater in a Sequencing Batch Packed-Bed Biofilm Reactor

Although the role of extracellular polymeric substances (EPSs) as a viscous high-molecular polymer in biological wastewater treatment has been recognized, in-depth knowledge of how EPSs affect nitrogen removal remains limited in biofilm-based reactors. Herein, we explored EPS characteristics associated with nitrogen removal from high-ammonia (NH4+-N: 300 mg/L) and low carbon-to-nitrogen ratio (C/N: 2–3) wastewater in a sequencing batch packed-bed biofilm reactor (SBPBBR) under four different operating scenarios for a total of 112 cycles. Scanning electron microscopy (SEM), atomic force microscopy (AFM), and Fourier-transform infrared (FTIR) analysis revealed that the distinct physicochemical properties, interface microstructure, and chemical composition of the bio-carrier were conducive to biofilm formation and microbial immobilization and enrichment. Under the optimal conditions (C/N: 3, dissolved oxygen: 1.3 mg/L, and cycle time: 12 h), 88.9% ammonia removal efficiency (ARE) and 81.9% nitrogen removal efficiency (NRE) could be achieved in the SBPBBR. Based on visual and SEM observations of the bio-carriers, biofilm development, biomass concentration, and microbial morphology were closely linked with nitrogen removal performance. Moreover, FTIR and three-dimensional excitation–emission matrix (3D-EEM) spectroscopy demonstrated that tightly bound EPSs (TB-EPSs) play a more important role in maintaining the stability of the biofilm. Significant shifts in the number, intensity, and position of fluorescence peaks of EPSs determined different nitrogen removal. More importantly, the high presence of tryptophan proteins and humic acids might promote advanced nitrogen removal. These findings uncover intrinsic correlations between EPSs and nitrogen removal for better controlling and optimizing biofilm reactors.
Why does the subsolar magnetopause move sunward for radial interplanetary magnetic field

[1] This paper analyzes the distribution of different pressure components (dynamic, thermal, magnetic) in the magnetosheath along the Sun-Earth line for northward and radial interplanetary magnetic field (IMF) orientations with motivation to explain an unusual location and shape of the magnetopause often observed during the intervals of the radial IMF. The analysis employs isotropic and anisotropic MHD models, and their results are compared with the statistical processing of THEMIS observations. We have found that (1) the temperature anisotropy in the magnetosheath controls the pressure distribution near the magnetopause, and (2) the total pressure exerted on the subsolar magnetopause depends on the IMF orientation being significantly lower than the solar wind dynamic pressure for the radial IMF. The results of both MHD simulations and statistical investigations are quantitatively consistent with already published observations of the average magnetopause location as a function of the IMF orientation.
A Sparse Multi-Scale Convolutional Neural Network to Classify Functional Connectivity for Brain Disease Diagnosis

Adopting deep learning methods to classify functional connectivity (FC) patterns for brain disease diagnosis has become a new research hotspot. Recently, deep neural network (DNN) with fully connected structure has been widely applied. However, the dense connections between adjacent layers bring abundant trainable parameters. Moreover, the current DNN models only extract FC features along one dimension, which lack potential multi-channel descriptions for the FC features. With the rapid development of convolutional neural network (CNN), various CNN-based deep learning models have been applied in pattern recognition. In this study, we proposed a sparse multi-scale CNN (SMS-CNN) model to classify FC patterns for brain disease diagnosis. In our model, multi-scale convolution operations were conducted to aggregate the FC features from different scales of spatial neighborhoods. In addition, to further alleviate over-fitting, we designed a lightweight feature sparse layer to scale the redundant FC features and increase the sparsity of the model. We conducted systematic experiments on the large-scale Autism Brain Imaging Data Exchange (ABIDE) dataset to validate the classification performance of the proposed model. Experimental results showed that the proposed SMS-CNN can better learning and classifying FC patterns and achieved high classification performance in distinguishing autism patients from healthy controls. Our study indicates the potential of CNN-based models in FC pattern analysis, and provides a promising method to further improve the classification performance for FC-based brain disease diagnosis.
Effect of Fluoroscopic-Guided Corticosteroid Injection in Patients With Sacroiliac Joint Dysfunction

Background Sacroiliac joint dysfunction is a major cause of axial low back pain which can masquerade as pain from lumbar disc diseases. Treatment of axial back pain arising due to sacroiliac joint dysfunction remains a challenge. This study was conducted to evaluate the long and short-term effects of intra-articular corticosteroid injection in the relief of pain and disability caused by sacroiliac joint dysfunction. Methodology A total of 83 patients with sacroiliac joint dysfunction were included in this prospective randomized control study. Patients were randomized into two groups by a computer-generated randomization table. These two groups were treated with fluoroscopy-guided corticosteroid and local anesthetic injection (group A) and distilled water and local anesthetic injection (group B). Pre and post-intervention assessment of all patients was done based on the Numeric Pain Rating Scale (NPRS) for pain and Oswestry Disability Index (ODI) for disability. The outcome measures of the study were the NPRS and ODI assessed at the initial visit one (pre-injection), two weeks post-injection (visit 2), and four weeks post-injection (visit 3). Results Demographic data were comparable in both groups. There was no significant difference in pre-injection NPRS and ODI values in both groups. The changes in NPRS and ODI values were significant from pre-injection to two weeks to four weeks. Group A patients performed better in terms of a decrease in the perception of pain and a decrease in the perception of disability compared to group B patients in the second and fourth weeks of follow-up. Conclusions Fluoroscopy-guided corticosteroid injection is an effective measure for reducing pain and disability in patients with sacroiliac joint dysfunction.
Scene Augmentation Methods for Interactive Embodied AI Tasks

The emerging embodied artificial intelligence (AI) paradigm enables intelligent robots to learn like humans from interaction, and is thus considered an effective way approach to general AI. Unfortunately, even the best-performing agents still overfit and generalize poorly to unseen scenes, due to the limited scenes provided by embodied AI simulators. To alleviate this issue, we propose a scene augmentation (SA) strategy to scale up the scene diversity for interactive tasks and make the interactions more like the real world. Compared to existing methods focusing on improving diversity in observation space, our approach aims to automatically derive a new distribution of scene layout or object states to provide sufficient conditional transfer models for the agent to learn environmental invariant and irrelevant features through interaction. Specifically, we provide four representative and systematical SA methods that can derive scene variants for entities from different levels of a scene graph. We demonstrate the efficiency of our methods in the popular embodied AI simulator iGibson. To verify the effectiveness of interactive agents, we also conduct two representative interactive tasks with a proposed continuous action parameterized method. The evaluation results show that our SA strategy can boost the performance of interactive agents and generalize well to unseen scenes. Finally, we present a systematic generalization analysis using the proposed methods to explicitly estimate the ability of agents to generalize to new layouts, new objects, and new object states. We claim that the proposed methods are not limited to iGibson and can be extended to other interactive simulators. The code and additional information are available at: https://github.com/sanghongrui/SceneAug.
RBF neural network approach for detecting land-cover transitions

The problem of detecting land-cover transitions in multitemporal remote-sensing images by exploiting the Bayes rule for compound classification is addressed. In particular, a novel method is proposed, which is based on the use of radial basis function (RBF) neural networks. The proposed method is composed of three main steps: i) the statistical terms involved in the compound classification rule are estimated separately on each single image by using the available training set and a standard procedure for the training of RBF neural networks; ii) the joint class probabilities are roughly initialised according to a simple estimation procedure; iii) the estimation of all parameters of the networks are iteratively improved by formulating the estimation process in terms of expectation-maximisation algorithm. The main advantages of the proposed approach with respect to our previous work on this topic are the following: i) to jointly perform and optimise the estimation process of all the required statistical terms; ii) to use both labelled and unlabeled samples in the estimation of all the required statistical terms (this is made possible by the use of a specific RBF architecture); iii) to better model the temporal correlation between classes in multitemporal images by replacing the class conditional independence assumption with a less restrictive one. Experiments, carried out on a multitemporal remote-sensing data set, confirm the effectiveness of the proposed automatic approach.
A Study of Mobile Network User Trajectory Fitting

Main technologies of mobile network user positioning are introduced. Two improvements to positioning are proposed and verified. Based on the characteristics of the coarse positioning and MR positioning data, the trajectory fitting algorithms are designed respectively. With the user's road test trajectory data collected by the self-developed mobile APP, qualitative and quantitative evaluation of the two fitting algorithms is carried out, verifying that these algorithms can effectively improve the similarity with the actual trajectories.
FlexTDOA: Robust and Scalable Time-Difference of Arrival Localization Using Ultra-Wideband Devices

In this paper, we propose FlexTDOA, an indoor localization method using ultra-wideband (UWB) radios, and we demonstrate its performance in a functional system. Our method uses time-difference of arrival (TDOA) localization so that the user device remains passive and is able to compute its location simply by listening to the communication between the fixed anchors, ensuring the scalability of the system. The anchors communicate using a custom and flexible time-division multiple-access (TDMA) scheme in which time is divided in slots. In each time slot, one anchor interrogates one or more anchors which respond in the same slot. The anchors do not need to have their clocks synchronized. We implemented FlexTDOA on in-house designed hardware using a commercial UWB module. We evaluate the localization accuracy of FlexTDOA with different system parameters such as the number of responses, the order of responses, and the number of anchors. We simulate and evaluate the effect of the physical speed of the tag on the choice of optimum system parameters. We also compare FlexTDOA against the classic TDOA approach and range-based localization in a deployment of ten anchors and one tag, both with and without obstructions. Results show that FlexTDOA achieves the highest localization accuracy in most of the scenarios, with up to 38% reduction in the localization error compared to the classic approach.
Health- or Environmental-Focused Text Messages to Increase Consumption of a Sustainable Diet among Young Adults: Importance of Expected Taste

Taste is a frequently cited barrier to the greater adoption of plant-based foods, a dietary pattern associated with both health and environmental benefits. To examine the role of expected taste in promoting greater adoption of plant-based foods, we examined the impact of a text-message intervention on the expected taste of both meat- and plant-protein foods. Young adults (n = 159) were randomly assigned to receive either health- or environment-focused text messages twice a week for eight weeks. Study measures (pre- and post-) included dietary recalls, the expected tastiness of meat- and plant-protein images and plant-based diets, consumption intention, and person-related factors such as moral satisfaction and the subjective norms of plant-based eating and health and environmental values. Participants rating plant-protein foods tastier at baseline were more likely to report higher actual (p < 0.001) and intended (p = 0.017) consumption of plant proteins following the intervention. While text messages had a limited effect on altering the expected taste of specific plant-protein foods, the messages did elevate the expected tastiness of plant-based diets. Baseline person-related factors positively predicted changes in expected tastiness of plant-based diets. Messages promoting plant-based foods may be more effective if these foods are first perceived as tasty. Furthermore, incorporating person-related considerations into messaging strategies may improve the expected taste of plant-based foods.
Positive irrational beliefs are associated with hypomanic personality

Primary irrational beliefs, such as demanding about attaining personal goals, are a common trans-diagnostic factor involved in many emotional disorders. Although Bipolar Disorder (BPD) is a severe emotional disorder, little is known about the role of primary irrational beliefs in the risk of BPD. Given that the risk for mania is related to responses to positive rather than adverse events, we developed a measure of irrational beliefs in response to cues of positive events. This is the first study that examines the relationship between positive primary irrational beliefs and the risk of BPD. 119 participants completed an online survey including measures for the risk of BPD, irrational beliefs, positive irrational beliefs, mania-related cognitions, and mood measures (depressive and manic mood). Results revealed significant associations between the risk of BPD and positive primary irrational beliefs, irrational beliefs, positive generalization, and mood. Regression analyses revealed that positive primary irrational beliefs, such as demanding to attain significant goals in response to cues for positive events, uniquely predict the risk for BPD independently of mood, mania-related cognitions and irrational beliefs. These findings encourage the treatment approaches focused on restructuring primary irrational beliefs in response to positive situations to reduce the risk of BPD.
Eccentric gravitational instabilities in nearly Keplerian disks

The growth of global gravitational instabilities in young stellar objects (YSOs) with associated circumstellar disks is studied. The possibility that the accretion ultimately owes its origin to the growth of spiral gravitational instabilities is explored. The results indicate that YSO disks will be unstable to the growth of eccentric distortions which have growth rates comparable to the orbital frequency at the outer edge of the disk. Thus, the distortions grow on nearly a dynamical time scale. Perturbations with m = 1 force the star to move from the center of mass and thereby transfer angular momentum to the stellar orbit. Depending on whether or not an axisymmetric stability parameter Q barrier exists near the corotation radius of the disturbance, this coupling may lead to mass accretion or to the formation of a binary companion from the disk, or both. 50 refs.
A Flow Attack Strategy based on Critical Links for Cyber-attack

Whether it be congestion control in cities or massive access in the Internet, these dynamic behaviors can be abstracted as flow demand between origin-destination node pairs (OD pairs) in the network. Links in complex systems are with notable heterogeneity, which means the volume of flow varies greatly, resulting in some critical links being more likely to congest under the flow attack, reducing the service capability of the system, and even causing network collapses. To explore how flow dynamics influences the network functionality and stability under congestion, we propose a link-based flow attack strategy that significantly degrades the service capability between OD pairs. In this approach, we first extract the routing paths and score the vulnerability of links between OD pairs, then an attack flow allocation rule based on critical links is designed to efficiently attack the target flow between OD pairs. Experiments on real-world networks show that the proposed strategy can quickly identify critical links and accurately attack the target flow. Besides, the proposed method provides positive and unique insights for defense strategy and network topology optimization.
Social Mobility and Vocational Outcomes: A Psychology of Working Perspective

The current study was aimed at understanding how social mobility relates to vocational outcomes. Drawing from Psychology of Working Theory (PWT), social mobility was operationalized as changes in experiences of economic constraints and marginalization between childhood and adulthood. We used latent profile analysis to create profiles among a sample of employed adults (N = 533) and compared profile membership to PWT informed outcomes: work volition, career adaptability, and decent work. The five profiles of social mobility that emerged were as follows: sustained privilege (low levels of marginalization and economic constraints during childhood and adulthood), downward mobility (low levels of marginalization and economic constraints in childhood, high levels in adulthood), upward mobility (high levels of marginalization and economic constraints in childhood, low levels in adulthood), highly marginalized (high levels of marginalization at both points), and sustained barriers (high levels of marginalization and economic constraints in childhood and adulthood). Participants who belonged to the sustained privilege and upward mobility groups experienced greater work volition and decent work compared to those in groups who experienced higher levels of economic constraints and marginalization throughout their lives. Implications for research and practice are discussed.