Origin and evolution of the News Finds Me perception: Review of theory and effects

Research revolving social media and democracy has exploded. For almost two decades, scholarship has offered new theories, revisited some old ones, and provided empirical evidence that helped cast a strong light on social media effects over people’s social life, and democracy at large. Thanks to social media, citizens consume news, express their political views, discuss political matters, and participate in political activities. However, social media also cultivates the dissemination of fake news and misinformation, exposure to hate speech, media fragmentation, and political polarization. In short, social media seems to simultaneously be a springboard for encouraging and undesirable outcomes that foster and challenge democracies alike. One of these phenomena that stems from social media news use is the News Finds Me perception (NFM), which takes place when individuals feel they do not have to actively seeks news any more to be well-informed about public affairs, as they expect to receive relevant news and information by relying on their peers in social media. This article traces back the origin of the theory, its evolution, and the set of effects found in the literature. It also presents guidelines for future research and potential challenges as the scholarship centering on NFM continues to grow

Decoupled Mixup for Data-efficient Learning

Mixup is an efficient data augmentation approach that improves the generalization of neural networks by smoothing the decision boundary with mixed data. Recently, dynamic mixup methods have improved previous static policies effectively (e.g., linear interpolation) by maximizing salient regions or maintaining the target in mixed samples. The discrepancy is that the generated mixed samples from dynamic policies are more instance discriminative than the static ones, e.g., the foreground objects are decoupled from the background. However, optimizing mixup policies with dynamic methods in input space is an expensive computation compared to static ones. Hence, we are trying to transfer the decoupling mechanism of dynamic methods from the data level to the objective function level and propose the general decoupled mixup (DM) loss. The primary effect is that DM can adaptively focus on discriminative features without losing the original smoothness of the mixup while avoiding heavy computational overhead. As a result, DM enables static mixup methods to achieve comparable or even exceed the performance of dynamic methods. This also leads to an interesting objective design problem for mixup training that we need to focus on both smoothing the decision boundaries and identifying discriminative features. Extensive experiments on supervised and semi-supervised learning benchmarks across seven classification datasets validate the effectiveness of DM by equipping it with various mixup methods.Comment: The preprint revision, 15 pages, 6 figures. The source code is available at https://github.com/Westlake-AI/openmixu

Phase-Modulated Elastic Properties of Two-Dimensional Magnetic FeTe: Hexagonal and Tetragonal Polymorphs

Two-dimensional (2D) layered magnets, such as iron chalcogenides, have emerged these years as a new family of unconventional superconductor and provided the key insights to understand the phonon-electron interaction and pairing mechanism. Their mechanical properties are of strategic importance for the potential applications in spintronics and optoelectronics. However, there is still lack of efficient approach to tune the elastic modulus despite the extensive studies. Herein, we report the modulated elastic modulus of 2D magnetic FeTe and its thickness-dependence via phase engineering. The grown 2D FeTe by chemical vapor deposition can present various polymorphs, i.e. tetragonal FeTe (t-FeTe, antiferromagnetic) and hexagonal FeTe (h-FeTe, ferromagnetic). The measured Young's modulus of t-FeTe by nanoindentation method showed an obvious thickness-dependence, from 290.9+-9.2 to 113.0+-8.7 GPa when the thicknesses increased from 13.2 to 42.5 nm, respectively. In comparison, the elastic modulus of h-FeTe remains unchanged. Our results could shed light on the efficient modulation of mechanical properties of 2D magnetic materials and pave the avenues for their practical applications in nanodevices.Comment: 19 pages, 4 figure

OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning

Spatio-temporal predictive learning is a learning paradigm that enables models to learn spatial and temporal patterns by predicting future frames from given past frames in an unsupervised manner. Despite remarkable progress in recent years, a lack of systematic understanding persists due to the diverse settings, complex implementation, and difficult reproducibility. Without standardization, comparisons can be unfair and insights inconclusive. To address this dilemma, we propose OpenSTL, a comprehensive benchmark for spatio-temporal predictive learning that categorizes prevalent approaches into recurrent-based and recurrent-free models. OpenSTL provides a modular and extensible framework implementing various state-of-the-art methods. We conduct standard evaluations on datasets across various domains, including synthetic moving object trajectory, human motion, driving scenes, traffic flow and weather forecasting. Based on our observations, we provide a detailed analysis of how model architecture and dataset properties affect spatio-temporal predictive learning performance. Surprisingly, we find that recurrent-free models achieve a good balance between efficiency and performance than recurrent models. Thus, we further extend the common MetaFormers to boost recurrent-free spatial-temporal predictive learning. We open-source the code and models at https://github.com/chengtan9907/OpenSTL.Comment: Accepted by NeurIPS 2023. 33 pages, 17 figures, 19 tables. Under review. For more details, please refer to https://github.com/chengtan9907/OpenST

Efficient Multi-order Gated Aggregation Network

Since the recent success of Vision Transformers (ViTs), explorations toward transformer-style architectures have triggered the resurgence of modern ConvNets. In this work, we explore the representation ability of DNNs through the lens of interaction complexities. We empirically show that interaction complexity is an overlooked but essential indicator for visual recognition. Accordingly, a new family of efficient ConvNets, named MogaNet, is presented to pursue informative context mining in pure ConvNet-based models, with preferable complexity-performance trade-offs. In MogaNet, interactions across multiple complexities are facilitated and contextualized by leveraging two specially designed aggregation blocks in both spatial and channel interaction spaces. Extensive studies are conducted on ImageNet classification, COCO object detection, and ADE20K semantic segmentation tasks. The results demonstrate that our MogaNet establishes new state-of-the-art over other popular methods in mainstream scenarios and all model scales. Typically, the lightweight MogaNet-T achieves 80.0\% top-1 accuracy with only 1.44G FLOPs using a refined training setup on ImageNet-1K, surpassing ParC-Net-S by 1.4\% accuracy but saving 59\% (2.04G) FLOPs.Comment: Preprint with 14 pages of main body and 5 pages of appendi

Smith-Purcell radiation from time grating

Smith-Purcell radiation (SPR) occurs when an electron skims above a spatial grating, but the fixed momentum compensation from the static grating imposes limitations on the emission wavelength. It has been discovered that a temporally periodic system can provide energy compensation to generate light emissions in free space. Here, we introduce temporal SPR (t-SPR) emerging from a time grating and propose a generalized t-SPR dispersion equation to predict the relationship between radiation frequency, direction, electron velocity, modulation period, and harmonic orders. Compared to conventional SPR, t-SPR can: 1) Provide a versatile platform for manipulating SPR emission through temporal modulation (e.g., period, amplitude, wave shape). 2) Exhibit strong robustness to the electron-grating separation, alleviating the constraints associated with extreme electron near-field excitation. 3) Introduce additional energy channels through temporal modulation, enhancing and amplifying emission.Comment: 6 pages, 3 figure

Associations between computed tomography markers of cerebral small vessel disease and hemorrhagic transformation after intravenous thrombolysis in acute ischemic stroke patients

BackgroundHemorrhagic transformation (HT) is common among acute ischemic stroke patients after treatment with intravenous thrombolysis (IVT). We analyzed potential relationships between markers of cerebral small vessel disease (CSVD) and HT in patients after IVT.MethodsThis study retrospectively analyzed computed tomography (CT) data for acute ischemic stroke patients before and after treatment with recombinant tissue plasminogen activator at a large Chinese hospital between July 2014 and June 2021. Total CSVD score were summed by individual CSVD markers including leukoaraiosis, brain atrophy and lacune. Binary regression analysis was used to explore whether CSVD markers were related to HT as the primary outcome or to symptomatic intracranial hemorrhage (sICH) as a secondary outcome.ResultsA total of 397 AIS patients treated with IVT were screened for inclusion in this study. Patients with missing laboratory data (n = 37) and patients treated with endovascular therapy (n = 42) were excluded. Of the 318 patients included, 54 (17.0%) developed HT within 24–36 h of IVT, and 14 (4.3%) developed sICH. HT risk was independently associated with severe brain atrophy (OR 3.14, 95%CI 1.43–6.92, P = 0.004) and severe leukoaraiosis (OR 2.41, 95%CI 1.05–5.50, P = 0.036), but not to severe lacune level (OR 0.58, 95%CI 0.23–1.45, P = 0.250). Patients with a total CSVD burden ≥1 were at higher risk of HT (OR 2.87, 95%CI 1.38–5.94, P = 0.005). However, occurrence of sICH was not predicted by CSVD markers or total CSVD burden.ConclusionIn patients with acute ischemic stroke, severe leukoaraiosis, brain atrophy and total CSVD burden may be risk factors for HT after IVT. These findings may help improve efforts to mitigate or even prevent HT in vulnerable patients

Effects of high-intensity interval training, moderate-intensity continuous training, and guideline-based physical activity on cardiovascular metabolic markers, cognitive and motor function in elderly sedentary patients with type 2 diabetes (HIIT-DM): a protocol for a randomized controlled trial

Background and objectiveSedentary behavior is of increasing concern in older patients with type 2 diabetes mellitus (T2DM) due to its potential adverse effects on cardiovascular health, cognitive function, and motor function. While regular exercise has been shown to improve the health of individuals with T2DM, the most effective exercise program for elderly sedentary patients with T2DM remains unclear. Therefore, the objective of this study was to assess the impact of high-intensity interval training (HIIT), moderate-intensity continuous training (MICT), and guideline-based physical activity programs on the cardiovascular health, cognitive function, and motor function of this specific population.MethodsThis study will be a randomized, assessor-blind, three-arm controlled trial. A total of 330 (1:1:1) elderly sedentary patients diagnosed with T2DM will be randomly assigned the HIIT group (10 × 1-min at 85–95% peak HR, intersperse with 1-min active recovery at 60–70% peak HR), MICT (35 min at 65–75% peak HR), and guideline-based group (guideline group) for 12 weeks training. Participants in the guideline group will receive 1-time advice and weekly remote supervision through smartphones. The primary outcomes will be the change in glycosylated hemoglobin (HbA1c) and brain-derived neurotrophic factor (BDNF) after 12-weeks. Secondary outcomes will includes physical activity levels, anthropometric parameters (weight, waist circumference, hip circumference, and body mass index), physical measurements (fat percentage, muscle percentage, and fitness rate), cardiorespiratory fitness indicators (blood pressure, heart rate, vital capacity, and maximum oxygen), biochemical markers (high-density lipoprotein, low-density lipoprotein, triglycerides, total cholesterol, and HbA1c), inflammation level (C-reactive protein), cognitive function (reaction time and dual-task gait test performance), and motor function (static balance, dynamic balance, single-task gait test performance, and grip strength) after 12 weeks.DiscussionThe objective of this study is to evaluate the effect of 12 weeks of HIIT, MICT, and a guideline-based physical activity program on elderly sedentary patients diagnosed with T2DM. Our hypothesis is that both HIIT and MICT will yield improvements in glucose control, cognitive function, cardiopulmonary function, metabolite levels, motor function, and physical fitness compared to the guideline group. Additionally, we anticipate that HIIT will lead to greater benefits in these areas. The findings from this study will provide valuable insights into the selection of appropriate exercise regimens for elderly sedentary individuals with T2DM.Ethics and disseminationThis study has been approved by the Ethics Review Committee of the Reproductive Hospital Affiliated with China Medical University (approval number: 202203). Informed consent will be obtained from all participants or their guardians. Upon completion, the authors will submit their findings to a peer-reviewed journal or academic conference for publication.Clinical trial registrationChinese Clinical Trial Registry, identifier ChiCTR2200061573