Gravity Effects on Information Filtering and Network Evolving

In this paper, based on the gravity principle of classical physics, we propose a tunable gravity-based model, which considers tag usage pattern to weigh both the mass and distance of network nodes. We then apply this model in solving the problems of information filtering and network evolving. Experimental results on two real-world data sets, \emph{Del.icio.us} and \emph{MovieLens}, show that it can not only enhance the algorithmic performance, but can also better characterize the properties of real networks. This work may shed some light on the in-depth understanding of the effect of gravity model

Construction and Accuracy Analysis of a BDS/GPS-Integrated Positioning Algorithm for Forests

The objective of this study was to construct a BeiDou navigation satellite system (BDS)/ global positioning system (GPS)-integrated positioning algorithm that meets the accuracy requirement of forest surveys and to analyze its accuracy to provide theoretical and technical support for accurate positioning and navigation in forests. The Quercus variabilis broad-leaved forest in Jiufeng National Forest Park and the Sabina Coniferous forest in Dongsheng Bajia forest farm were selected as the study area. A Sanding T-23 multi-frequency three-constellation receiver and a u-blox NEO-M8T multi-constellation receiving module were used for continuous observation under the forest canopy. Compared with T-23, the u-blox NEO-M8T is much lighter and more flexible in the forest. The BDS/GPS-integrated positioning algorithm for forests was constructed by temporally and spatially unifying the satellite systems and using a reasonable observed value weighting method. Additionally, the algorithm is also written into the RTKLIB software to calculate the three-dimensional (3D) coordinates of the forest observation point in the World Geodetic System 1984 (WGS-84) coordinate system. Finally, the results were compared with the positioning results obtained using GPS alone. The experimental results indicated that, compared with GPS positioning, there were 13–27 visible satellites available for the BDS/GPS-integrated positioning algorithm for forests, far more than the satellites available for the GPS positioning algorithm alone. The Position Dilution of Precision (PDOP) values for the BDS/GPS-integrated positioning ranged from 0.5 to 1.9, lower than those for GPS positioning. The signal noise ratio (SNR) of the BDS/GPS-integrated satellite signals and GPS satellite signals were both in the range of 10–50 dB-Hz. However, because there were more visible satellites for the BDS/GPS-integrated positioning, the signals from the BDS/GPS-integrated satellites were stronger and had a more stable SNR than those from the GPS satellites alone. The results obtained using the BDS/GPS-integrated positioning algorithm for forests had significantly higher theoretical and actual accuracies in the X, Y and Z directions than those obtained using the GPS positioning algorithm. This suggests that the BDS/GPS-integrated positioning algorithm can obtain more accurate positioning results for complex forest environments

Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks

Graph Neural Networks (GNNs) tend to suffer from high computation costs due to the exponentially increasing scale of graph data and the number of model parameters, which restricts their utility in practical applications. To this end, some recent works focus on sparsifying GNNs with the lottery ticket hypothesis (LTH) to reduce inference costs while maintaining performance levels. However, the LTH-based methods suffer from two major drawbacks: 1) they require exhaustive and iterative training of dense models, resulting in an extremely large training computation cost, and 2) they only trim graph structures and model parameters but ignore the node feature dimension, where significant redundancy exists. To overcome the above limitations, we propose a comprehensive graph gradual pruning framework termed CGP. This is achieved by designing a during-training graph pruning paradigm to dynamically prune GNNs within one training process. Unlike LTH-based methods, the proposed CGP approach requires no re-training, which significantly reduces the computation costs. Furthermore, we design a co-sparsifying strategy to comprehensively trim all three core elements of GNNs: graph structures, node features, and model parameters. Meanwhile, aiming at refining the pruning operation, we introduce a regrowth process into our CGP framework, in order to re-establish the pruned but important connections. The proposed CGP is evaluated by using a node classification task across 6 GNN architectures, including shallow models (GCN and GAT), shallow-but-deep-propagation models (SGC and APPNP), and deep models (GCNII and ResGCN), on a total of 14 real-world graph datasets, including large-scale graph datasets from the challenging Open Graph Benchmark. Experiments reveal that our proposed strategy greatly improves both training and inference efficiency while matching or even exceeding the accuracy of existing methods.Comment: 29 pages, 27 figures, submitting to IEEE TNNL

ProtoEM: A Prototype-Enhanced Matching Framework for Event Relation Extraction

Event Relation Extraction (ERE) aims to extract multiple kinds of relations among events in texts. However, existing methods singly categorize event relations as different classes, which are inadequately capturing the intrinsic semantics of these relations. To comprehensively understand their intrinsic semantics, in this paper, we obtain prototype representations for each type of event relation and propose a Prototype-Enhanced Matching (ProtoEM) framework for the joint extraction of multiple kinds of event relations. Specifically, ProtoEM extracts event relations in a two-step manner, i.e., prototype representing and prototype matching. In the first step, to capture the connotations of different event relations, ProtoEM utilizes examples to represent the prototypes corresponding to these relations. Subsequently, to capture the interdependence among event relations, it constructs a dependency graph for the prototypes corresponding to these relations and utilized a Graph Neural Network (GNN)-based module for modeling. In the second step, it obtains the representations of new event pairs and calculates their similarity with those prototypes obtained in the first step to evaluate which types of event relations they belong to. Experimental results on the MAVEN-ERE dataset demonstrate that the proposed ProtoEM framework can effectively represent the prototypes of event relations and further obtain a significant improvement over baseline models.Comment: Work in progres

Three Dimensional Reconfigurable Optical Singularities in Bilayer Photonic Crystals

Metasurfaces and photonic crystals have revolutionized classical and quantum manipulation of light, and opened the door to studying various optical singularities related to phases and polarization states. However, traditional nanophotonic devices lack reconfigurability, hindering the dynamic switching and optimization of optical singularities. This paper delves into the underexplored concept of tunable bilayer photonic crystals (BPhCs), which offer rich interlayer coupling effects. Utilizing silicon nitride-based BPhCs, we demonstrate tunable bidirectional and unidirectional polarization singularities, along with spatiotemporal phase singularities. Leveraging these tunable singularities, we achieve dynamic modulation of bound-state-in-continuum states, unidirectional guided resonances, and both longitudinal and transverse orbital angular momentum. Our work paves the way for multidimensional control over polarization and phase, inspiring new directions in ultrafast optics, optoelectronics, and quantum optics

Quantum control of exciton wavefunctions in 2D semiconductors

Excitons -- bound electron-hole pairs -- play a central role in light-matter interaction phenomena, and are crucial for wide-ranging applications from light harvesting and generation to quantum information processing. A long-standing challenge in solid-state optics has been to achieve precise and scalable control over the quantum mechanical state of excitons in semiconductor heterostructures. Here, we demonstrate a technique for creating tailored and tunable potential landscapes for optically active excitons in 2D semiconductors that enables in-situ wavefunction shaping at the nanoscopic lengthscale. Using nanostructured gate electrodes, we create localized electrostatic traps for excitons in diverse geometries such as quantum dots and rings, and arrays thereof. We show independent spectral tuning of multiple spatially separated quantum dots, which allows us to bring them to degeneracy despite material disorder. Owing to the strong light-matter coupling of excitons in 2D semiconductors, we observe unambiguous signatures of confined exciton wavefunctions in optical reflection and photoluminescence measurements. Our work introduces a new approach to engineering exciton dynamics and interactions at the nanometer scale, with implications for novel optoelectronic devices, topological photonics, and many-body quantum nonlinear optics

Identification of Human ABAD Inhibitors for Rescuing Aβ-Mediated Mitochondrial Dysfunction

Amyloid beta (Aβ) binding alcohol dehydrogenase (ABAD) is a cellular cofactor for promoting (Aβ)-mediated mitochondrial and neuronal dysfunction, and cognitive decline in transgenic Alzheimer's disease (AD) mouse models. Targeting mitochondrial ABAD may represent a novel therapeutic strategy against AD. Here, we report the biological activity of small molecule ABAD inhibitors. Using in vitro surface plasmon resonance (SPR) studies, we synthesized compounds with strong binding affinities for ABAD. Further, these ABAD inhibitors (ABAD-4a and 4b) reduced ABAD enzyme activity and administration of phosphonate derivatives of ABAD inhibitors antagonized calcium-mediated mitochondrial swelling. Importantly, these compounds also abolished Aβ-induced mitochondrial dysfunction as shown by increased cytochrome c oxidase and adenosine-5′-triphosphate levels, suggesting protective mitochondrial function effects of these synthesized compounds. Thus, these compounds are potential candidates for further pharmacologic development to target ABAD to improve mitochondrial function

Inhibition of ERK-DLP1 signaling and mitochondrial division alleviates mitochondrial dysfunction in Alzheimer's disease cybrid cell

Mitochondrial dysfunction is an early pathological feature of Alzheimer’s disease (AD). The underlying mechanisms and strategies to repair it remain unclear. Here, we demonstrate for the first time the direct consequences and potential mechanisms of mitochondrial functional defects associated with abnormal mitochondrial dynamics in AD. Using cytoplasmic hybrid (cybrid) neurons with incorporated platelet mitochondria from AD and age-matched non-AD human subjects into mitochondrial DNA (mtDNA)-depleted neuronal cells, we observed that AD cybrid cells had significant changes in morphology and function; such changes associate with altered expression and distribution of dynamin-like protein (DLP1) and mitofusin 2 (Mfn2). Treatment with antioxidant protects against AD mitochondria-induced extracellular signal-regulated kinase (ERK) activation and mitochondrial fission-fusion imbalances. Notably, inhibition of ERK activation not only attenuates aberrant mitochondrial morphology and function but also restores the mitochondrial fission and fusion balance. These effects suggest a role of oxidative stress-mediated ERK signal transduction in modulation of mitochondrial fission and fusion events. Further, blockade of the mitochondrial fission protein DLP1 by a genetic manipulation with a dominant negative DLP1 (DLP1K38A), its expression with siRNA-DLP1, or inhibition of mitochondrial division with mdivi-1 attenuates mitochondrial functional defects observed in AD cybrid cells. Our results provide new insights into mitochondrial dysfunction resulting from changes in the ERK-fission/fusion (DLP1) machinery and signaling pathway. The protective effect of mdivi-1 and inhibition of ERK signaling on maintenance of normal mitochondrial structure and function holds promise as a potential novel therapeutic strategy for AD

Inhibition of ERK-DLP1 signaling and mitochondrial division alleviates mitochondrial dysfunction in Alzheimer's disease cybrid cell

Mitochondrial dysfunction is an early pathological feature of Alzheimer’s disease (AD). The underlying mechanisms and strategies to repair it remain unclear. Here, we demonstrate for the first time the direct consequences and potential mechanisms of mitochondrial functional defects associated with abnormal mitochondrial dynamics in AD. Using cytoplasmic hybrid (cybrid) neurons with incorporated platelet mitochondria from AD and age-matched non-AD human subjects into mitochondrial DNA (mtDNA)-depleted neuronal cells, we observed that AD cybrid cells had significant changes in morphology and function; such changes associate with altered expression and distribution of dynamin-like protein (DLP1) and mitofusin 2 (Mfn2). Treatment with antioxidant protects against AD mitochondria-induced extracellular signal-regulated kinase (ERK) activation and mitochondrial fission-fusion imbalances. Notably, inhibition of ERK activation not only attenuates aberrant mitochondrial morphology and function but also restores the mitochondrial fission and fusion balance. These effects suggest a role of oxidative stress-mediated ERK signal transduction in modulation of mitochondrial fission and fusion events. Further, blockade of the mitochondrial fission protein DLP1 by a genetic manipulation with a dominant negative DLP1 (DLP1K38A), its expression with siRNA-DLP1, or inhibition of mitochondrial division with mdivi-1 attenuates mitochondrial functional defects observed in AD cybrid cells. Our results provide new insights into mitochondrial dysfunction resulting from changes in the ERK-fission/fusion (DLP1) machinery and signaling pathway. The protective effect of mdivi-1 and inhibition of ERK signaling on maintenance of normal mitochondrial structure and function holds promise as a potential novel therapeutic strategy for AD