Knowledge-Enhanced Hierarchical Information Correlation Learning for Multi-Modal Rumor Detection

The explosive growth of rumors with text and images on social media platforms has drawn great attention. Existing studies have made significant contributions to cross-modal information interaction and fusion, but they fail to fully explore hierarchical and complex semantic correlation across different modality content, severely limiting their performance on detecting multi-modal rumor. In this work, we propose a novel knowledge-enhanced hierarchical information correlation learning approach (KhiCL) for multi-modal rumor detection by jointly modeling the basic semantic correlation and high-order knowledge-enhanced entity correlation. Specifically, KhiCL exploits cross-modal joint dictionary to transfer the heterogeneous unimodality features into the common feature space and captures the basic cross-modal semantic consistency and inconsistency by a cross-modal fusion layer. Moreover, considering the description of multi-modal content is narrated around entities, KhiCL extracts visual and textual entities from images and text, and designs a knowledge relevance reasoning strategy to find the shortest semantic relevant path between each pair of entities in external knowledge graph, and absorbs all complementary contextual knowledge of other connected entities in this path for learning knowledge-enhanced entity representations. Furthermore, KhiCL utilizes a signed attention mechanism to model the knowledge-enhanced entity consistency and inconsistency of intra-modality and inter-modality entity pairs by measuring their corresponding semantic relevant distance. Extensive experiments have demonstrated the effectiveness of the proposed method

BAK overexpression mediates p53-independent apoptosis inducing effects on human gastric cancer cells

BACKGROUND: BAK (Bcl-2 homologous antagonist/killer) is a novel pro-apoptotic gene of the Bcl-2 family. It has been reported that gastric tumors have reduced BAK levels when compared with the normal mucosa. Moreover, mutations of the BAK gene have been identified in human gastrointestinal cancers, suggesting that a perturbation of BAK-mediated apoptosis may contribute to the pathogenesis of gastric cancer. In this study, we explored the therapeutic effects of gene transfer mediated elevations in BAK expression on human gastric cancer cells in vitro. METHODS: Eukaryotic expression vector for the BAK gene was constructed and transferred into gastric cancer cell lines, MKN-45 (wild-type p53) and MKN-28 (mutant-type p53). RT-PCR and Western Blotting detected cellular BAK gene expression. Cell growth activities were detected by MTT colorimetry and flow cytometry, while apoptosis was assayed by electronic microscopy and TUNEL. Western Blotting and colorimetry investigated cellular caspase-3 activities. RESULTS: BAK gene transfer could result in significant BAK overexpression, decreased in vitro growth, cell cycle G(0)/G(1 )arrest, and induced apoptosis in gastric cancer cells. In transferred cells, inactive caspase-3 precursor was cleaved into the active subunits p20 and p17, during BAK overexpression-induced apoptosis. In addition, this process occurred equally well in p53 wild-type (MKN-45), or in p53 mutant-type (MKN-28) gastric cancer cells. CONCLUSIONS: The data presented suggests that overexpression of the BAK gene can lead to apoptosis of gastric cancer cells in vitro, which does not appear to be dependent on p53 status. The action mechanism of BAK mediated apoptosis correlates with activation of caspase-3. This could be served as a potential strategy for further development of gastric cancer therapies

Non-Abelian Chiral Spin Liquid on the Kagome Lattice

We study $S=1$ spin liquid states on the kagome lattice constructed by Gutzwiller-projected $p_x+ip_y$ superconductors. We show that the obtained spin liquids are either non-Abelian or Abelian topological phases, depending on the topology of the fermionic mean-field state. By calculating the modular matrices $S$ and $T$, we confirm that projected topological superconductors are non-Abelian chiral spin liquid (NACSL). The chiral central charge and the spin Hall conductance we obtained agree very well with the $SO(3)_1$ (or, equivalently, $SU(2)_2$) field theory predictions. We propose a local Hamiltonian which may stabilize the NACSL. From a variational study we observe a topological phase transition from the NACSL to the $Z_2$ Abelian spin liquid.Comment: 12 pages, 7 figures, 1 tabl

Simultaneous Conversion of Polarization and Frequency via Time‐Division‐Multiplexing Metasurfaces

AbstractMetasurfaces are artificially engineered two‐dimensional materials composed of sub‐wavelength meta‐atoms, which have shown unprecedented capabilities in manipulating the amplitude, phase, frequency, and polarization states of electromagnetic waves. Specifically, polarization control can be attained via suitable anisotropic, linear, and time‐invariant designs, while frequency conversion is realized via nonlinear or time‐varying platforms. Simultaneous manipulations of polarization and frequency would be of considerable practical interest in many application scenarios, but remain unattainable with current approaches. Here, a time‐division‐multiplexing metasurface is proposed to realize the simultaneous conversion of polarization and frequency. The platform relies on time‐modulated polarization switches and, by varying the duty cycle and time delays of the polarization channels, can arbitrarily rotate the polarization at the central frequency of operation, and synthesize various polarization states at selected harmonic frequencies. Theoretical predictions are validated via measurements on a prototype operating at microwave frequencies, providing the first experimental evidence of simultaneous polarization and frequency conversions via time‐division‐multiplexing metasurfaces. The outcomes open a new pathway in manipulating the electromagnetic waves via time‐varying metasurfaces, and may be of interest for a broad variety of applications in scenarios ranging from polarization imaging to quantum optics

A Dependable Slepian-Wolf Coding Based Clustering Algorithm for Data Aggregation in Wireless Sensor Networks

International audienceThis paper considers the Slepian-Wolf coding based data aggregation problem and the corresponding dependable clustering problem in wireless sensor networks (WSNs). A dependable Slepian-Wolf coding based clustering (DSWC) algorithm is proposed to provide dependable clustering against cluster-head failures. The proposed D-SWC algorithm attempts to elect a primary cluster head and a backup cluster head for each cluster member during clustering so that once a failure occurs to the primary cluster head the cluster members within the failed cluster can promptly switchover to the backup cluster head and thus recover the connectivity of the failed cluster to the data sink without waiting for the next-round clustering to be performed. Simulation results show that the DSWC algorithm can effectively increase the amount of data transmitted to the data sink as compared with an existing nondependable clustering algorithm for Slepian-Wolf coding based data aggregation in WSNs