Exponential Mixing for Retarded Stochastic Differential Equations

In this paper, we discuss exponential mixing property for Markovian semigroups generated by segment processes associated with several class of retarded Stochastic Differential Equations (SDEs) which cover SDEs with constant/variable/distributed time-lags. In particular, we investigate the exponential mixing property for (a) non-autonomous retarded SDEs by the Arzel\`{a}--Ascoli tightness characterization of the space \C equipped with the uniform topology (b) neutral SDEs with continuous sample paths by a generalized Razumikhin-type argument and a stability-in-distribution approach and (c) jump-diffusion retarded SDEs by the Kurtz criterion of tightness for the space \D endowed with the Skorohod topology.Comment: 20 page

MSMG-Net: Multi-scale Multi-grained Supervised Metworks for Multi-task Image Manipulation Detection and Localization

With the rapid advances of image editing techniques in recent years, image manipulation detection has attracted considerable attention since the increasing security risks posed by tampered images. To address these challenges, a novel multi-scale multi-grained deep network (MSMG-Net) is proposed to automatically identify manipulated regions. In our MSMG-Net, a parallel multi-scale feature extraction structure is used to extract multi-scale features. Then the multi-grained feature learning is utilized to perceive object-level semantics relation of multi-scale features by introducing the shunted self-attention. To fuse multi-scale multi-grained features, global and local feature fusion block are designed for manipulated region segmentation by a bottom-up approach and multi-level feature aggregation block is designed for edge artifacts detection by a top-down approach. Thus, MSMG-Net can effectively perceive the object-level semantics and encode the edge artifact. Experimental results on five benchmark datasets justify the superior performance of the proposed method, outperforming state-of-the-art manipulation detection and localization methods. Extensive ablation experiments and feature visualization demonstrate the multi-scale multi-grained learning can present effective visual representations of manipulated regions. In addition, MSMG-Net shows better robustness when various post-processing methods further manipulate images

Higher-order Oscillatory Planar Hall Effect in Topological Kagome Metal

Exploration of exotic transport behavior for quantum materials is of great interest and importance for revealing exotic orders to bring new physics. In this Letter, we report the observation of exotic prominent planar Hall effect (PHE) and planar anisotropic magnetoresistivity (PAMR) in strange kagome metal KV$_3$Sb$_5$. The PHE and PAMR, which are driven by an in-plane magnetic field and display sharp difference from other Hall effects driven by an out-of-plane magnetic field or magnetization, exhibit exotic higher-order oscillations in sharp contrast to those following empirical rule only allowing twofold symmetrical oscillations. These higher-order oscillations exhibit strong field and temperature dependence and vanish around charge density wave (CDW) transition. The unique transport properties suggest a significant interplay of the lattice, magnetic and electronic structure in KV$_3$Sb$_5$. This interplay can couple the hidden anisotropy and transport electrons leading to the novel PHE and PAMR in contrast to other materials

Magneto-Transport Properties of Kagome Magnet TmMn6_6Sn6_6

Kagome magnet usually hosts nontrivial electronic or magnetic states drawing great interests in condensed matter physics. In this paper, we report a systematic study on transport properties of kagome magnet TmMn$_6$Sn$_6$. The prominent topological Hall effect (THE) has been observed in a wide temperature region spanning over several magnetic phases and exhibits strong temperature and field dependence. This novel phenomenon due to non-zero spin chirality indicates possible appearance of nontrival magnetic states accompanying with strong fluctuations. The planar applied field drives planar Hall effect(PHE) and anistropic magnetoresisitivity(PAMR) exhibiting sharp disconnections in angular dependent planar resistivity violating the empirical law. By using an effective field, we identify a magnetic transition separating the PAMR into two groups belonging to various magnetic states. We extended the empirical formula to scale the field and temperature dependent planar magnetoresistivity and provide the understandings for planar transport behaviors with the crossover between various magnetic states. Our results shed lights on the novel transport effects in presence of multiple nontrivial magnetic states for the kagome lattice with complicated magnetic structures

The Relationship Between Learning Motivation and Learning Anxiety of College Students

In contemporary society, the escalating anxiety among college students has emerged as a pressing social concern, impacting both their mental well-being and academic performance by influencing levels of learning motivation. This research posits a negative correlation between the burgeoning learning anxiety and the motivation to learn among college students. Employing a quantitative research approach, data is gathered through structured questionnaires, and subsequent analysis is conducted using SPSS statistical software to derive meaningful insights. The study primarily focuses on Chinese college students, aiming to unveil the intricate relationship between learning anxiety and motivation. Through this investigation, the researchers seek to formulate strategies that mitigate learning anxiety and concurrently bolster intrinsic motivation for sustained learning among Chinese college students. This research serves as a key step in understanding and addressing the contemporary challenges associated with the mental health and academic performance of college students, paving the way for interventions that foster a positive learning environment

Structural and electronic origin of the magnetic structures in hexagonal LuFeO3_3

Using combined theoretical and experimental approaches, we studied the structural and electronic origin of the magnetic structure in hexagonal LuFeO$_3$. Besides showing the strong exchange coupling that is consistent with the high magnetic ordering temperature, the previously observed spin reorientation transition is explained by the theoretically calculated magnetic phase diagram. The structural origin of this spin reorientation that is responsible for the appearance of spontaneous magnetization, is identified by theory and verified by x-ray diffraction and absorption experiments.Comment: 5 pages, 2 tables and 4 figures, Please contact us for the supplementary material. Accepted in Phys. Rev. B, in productio

Key Issues in Wireless Transmission for NTN-Assisted Internet of Things

Non-terrestrial networks (NTNs) have become appealing resolutions for seamless coverage in the next-generation wireless transmission, where a large number of Internet of Things (IoT) devices diversely distributed can be efficiently served. The explosively growing number of IoT devices brings a new challenge for massive connection. The long-distance wireless signal propagation in NTNs leads to severe path loss and large latency, where the accurate acquisition of channel state information (CSI) is another challenge, especially for fast-moving non-terrestrial base stations (NTBSs). Moreover, the scarcity of on-board resources of NTBSs is also a challenge for resource allocation. To this end, we investigate three key issues, where the existing schemes and emerging resolutions for these three key issues have been comprehensively presented. The first issue is to enable the massive connection by designing random access to establish the wireless link and multiple access to transmit data streams. The second issue is to accurately acquire CSI in various channel conditions by channel estimation and beam training, where orthogonal time frequency space modulation and dynamic codebooks are on focus. The third issue is to efficiently allocate the wireless resources, including power allocation, spectrum sharing, beam hopping, and beamforming. At the end of this article, some future research topics are identified.Comment: 7 pages, 6 figure

Aftertreatment control and adaptation for automotive lean burn engines with HEGO sensors

Control of aftertreatment systems for lean burn technology engines represents a big challenge, due to the lack of on-board emission measurements and the sensitivity of the hardware components to ageing and sulphur poisoning. In this paper, we consider the control and adaptation of aftertreatment systems involving lean NO x trap (LNT). A phenomenological LNT model is presented to facilitate the model-based control and adaptation. A control strategy based on the LNT model and HEGO (heated exhaust gas oxygen) sensor feedback is discussed. A linear parametric model, which is derived by exploiting the physical properties of the LNT is used for adaptation of trap capacity and feedgas NO x emission models. The conditions under which parameter convergence will be achieved are derived for the proposed adaptive scheme. Simulation results for different scenarios are included to demonstrate the effectiveness of control and adaptation. Copyright © 2004 John Wiley & Sons, Ltd.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/35013/1/786_ftp.pd