Fake News Detection Based on Text-Modal Dominance and Fusing Multiple Multi-Model Clues

In recent years,how to efficiently and accurately identify multi-model fake news has become more challenging.First,multi-model data provides more evidence but not all are equally important.Secondly,social structure information has proven to be effective in fake news detection and how to combine it while reducing the noise information is critical.Unfortunately,existing approaches fail to handle these problems.This paper proposes a multi-model fake news detection framework based on Tex-modal Dominance and fusing Multiple Multi-model Cues(TD-MMC),which utilizes three valuable multi-model clues:text-model importance,text-image complementary,and text-image inconsistency.TD-MMC is dominated by textural content and assisted by image information while using social network information to enhance text representation.To reduce the irrelevant social structure’s information interference,we use a unidirectional cross-modal attention mechanism to selectively learn the social structure’s features.A cross-modal attention mechanism is adopted to obtain text-image cross-modal features while retaining textual features to reduce the loss of important information.In addition,TD-MMC employs a new multi-model loss to improve the model’s generalization ability.Extensive experiments have been conducted on two public real-world English and Chinese datasets,and the results show that our proposed model outperforms the state-of-the-art methods on classification evaluation metrics.

A Multi-mode Electronic Load Sensing Control Scheme with Power Limitation and Pressure Cut-off for Mobile Machinery

In mobile machinery,hydro-mechanical pumps are increasingly replaced by electronically controlled pumps to improve the automation level,but diversified control functions(e.g.,power limitation and pressure cut-off)are integrated into the electronic controller only from the pump level,leading to the potential instability of the overall system.To solve this problem,a multi-mode electrohydraulic load sensing(MELS)control scheme is proposed especially considering the switching stability from the system level,which includes four working modes of flow control,load sensing,power limitation,and pressure control.Depending on the actual working requirements,the switching rules for the different modes and the switching direction(i.e.,the modes can be switched bilaterally or unilaterally)are defined.The priority of different modes is also defined,from high to low:pressure control,power limitation,load sensing,and flow control.When multiple switching rules are satisfied at the same time,the system switches to the control mode with the highest priority.In addition,the switching stability between flow control and pressure control modes is analyzed,and the controller parameters that guarantee the switching stability are obtained.A comparative study is carried out based on a test rig with a 2-ton hydraulic excavator.The results show that the MELS controller can achieve the control functions of proper flow supplement,power limitation,and pressure cut-off,which has good stability performance when switching between different control modes.This research proposes the MELS control method that realizes the stability of multi-mode switching of the hydraulic system of mobile machinery under different working conditions.

Templated synthesis of transition metal phosphide electrocatalysts for oxygen and hydrogen evolution reactions

Transition metal phosphides(TMPs)have been regarded as alternative hydrogen evolution reaction(HER)and oxygen evolution reaction(OER)catalysts owing to their comparable activity to those of noble metal-based catalysts.TMPs have been produced in various morphologies,including hollow and porous nanostructures,which are features deemed desirable for electrocatalytic materials.Templated synthesis routes are often responsible for such morphologies.This paper reviews the latest advances and existing challenges in the synthesis of TMP-based OER and HER catalysts through templated methods.A comprehensive review of the structure-property-performance of TMP-based HER and OER catalysts prepared using different templates is presented.The discussion proceeds according to application,first by HER and further divided among the types of templates used-from hard templates,sacrificial templates,and soft templates to the emerging dynamic hydrogen bubble template.OER catalysts are then reviewed and grouped according to their morphology.Finally,prospective research directions for the synthesis of hollow and porous TMP-based catalysts,such as improvements on both activity and stability of TMPs,design of environmentally benign templates and processes,and analysis of the reaction mechanism through advanced material characterization techniques and theoretical calculations,are suggested.

Multi-mode Multi-frequency GNSS-IR Combination System for Sea Level Retrieval

With the development of Global Navigation Satellite Systems(GNSS),geodetic GNSS receivers have been utilized to monitor sea levels using GNSS-Interferometry Reflectometry(GNSS-IR)technology.The multi-mode,multi-frequency signals of GPS,GLONASS,Galileo,and Beidou can be used for GNSS-IR sea level retrieval,but combining these retrievals remains problematic.To address this issue,a GNSS-IR sea level retrieval combination system has been developed,which begins by analyzing error sources in GNSS-IR sea level retrieval and establishing and solving the GNSS-IR retrieval equation.This paper focuses on two key points:time window selection and equation stability.The stability of the retrieval combination equations is determined by the condition number of the coefficient matrix within the time window.The impact of ill-conditioned coefficient matrices on the retrieval results is demonstrated using an extreme case of SNR data with only ascending or descending trajectories.After determining the time window and removing ill-conditioned equations,the multi-mode,multi-frequency GNSS-IR retrieval is performed.Results from three International GNSS Service(IGS)stations show that the combination method produces high-precision,high-resolution,and high-reliability sea level retrieval combination sequences.

Method of Multi-Mode Sensor Data Fusion with an Adaptive Deep Coupling Convolutional Auto-Encoder

To address the difficulties in fusing multi-mode sensor data for complex industrial machinery, an adaptive deep coupling convolutional auto-encoder (ADCCAE) fusion method was proposed. First, the multi-mode features extracted synchronously by the CCAE were stacked and fed to the multi-channel convolution layers for fusion. Then, the fused data was passed to all connection layers for compression and fed to the Softmax module for classification. Finally, the coupling loss function coefficients and the network parameters were optimized through an adaptive approach using the gray wolf optimization (GWO) algorithm. Experimental comparisons showed that the proposed ADCCAE fusion model was superior to existing models for multi-mode data fusion.

A Hand Features Based Fusion Recognition Network with Enhancing Multi-Modal Correlation

Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities.Additionally,it leverages inter-modal correlation to enhance recognition performance.Concurrently,the robustness and recognition performance of the system can be enhanced through judiciously leveraging the correlation among multimodal features.Nevertheless,two issues persist in multi-modal feature fusion recognition:Firstly,the enhancement of recognition performance in fusion recognition has not comprehensively considered the inter-modality correlations among distinct modalities.Secondly,during modal fusion,improper weight selection diminishes the salience of crucial modal features,thereby diminishing the overall recognition performance.To address these two issues,we introduce an enhanced DenseNet multimodal recognition network founded on feature-level fusion.The information from the three modalities is fused akin to RGB,and the input network augments the correlation between modes through channel correlation.Within the enhanced DenseNet network,the Efficient Channel Attention Network(ECA-Net)dynamically adjusts the weight of each channel to amplify the salience of crucial information in each modal feature.Depthwise separable convolution markedly reduces the training parameters and further enhances the feature correlation.Experimental evaluations were conducted on four multimodal databases,comprising six unimodal databases,including multispectral palmprint and palm vein databases from the Chinese Academy of Sciences.The Equal Error Rates(EER)values were 0.0149%,0.0150%,0.0099%,and 0.0050%,correspondingly.In comparison to other network methods for palmprint,palm vein,and finger vein fusion recognition,this approach substantially enhances recognition performance,rendering it suitable for high-security environments with practical applicability.The experiments in this article utilized amodest sample database comprising 200 individuals.The subsequent phase involves preparing for the extension of the method to larger databases.

New Rural Community Construction or Retention Development:A Comparative Analysis of Rural Settlement Transition Mechanism in Plain Agriculture Area of China Based on Actor Network Theory

It is an important way to realize rural revitalization and sustainable development to guide rural settlement transition(RST)in an appropriate way.This paper uses actor network theory(ANT)to construct a theoretical framework for the study of RST.Taking two typical villages with different transition paths in rural areas of North China Plain as examples,this paper reveals the mechanism of RST and makes a comparative analysis.The results show that:1)after identifying problems and obligatory passage point,key actors recruit heterogeneous actors into the actor network by entrusting them with common interests,and realize RST under the system operation.2)Rural settlements under different transition paths have similarities in the problems to be solved,collective actions and policy factors,but there are differences in the transition process,mechanism and effect.The actor network and mechanism of RST through the path of new rural community construction are more complex and the transition effect is more thorough.In contrast,the degree of RST of retention development path is limited if there is no resource and location advantage.3)Based on the applicable conditions of different paths,this paper designs a logical framework of‘Situation-Structure-Behavior-Result’to scientifically guide the identification of RST paths under the background of rural revitalization.

Photodoping-Modified Charge Density Wave Phase Transition in WS_(2)/1T-TaS_(2) Heterostructure

Controlling collective electronic states hold great promise for development of innovative devices. Here, we experimentally detect the modification of the charge density wave(CDW) phase transition within a 1T-TaS_(2) layer in a WS_(2)/1T-TaS_(2) heterostructure using time-resolved ultrafast spectroscopy. Laser-induced charge transfer doping strongly suppresses the commensurate CDW phase, which results in a significant decrease in both the phase transition temperature(T_(c)) and phase transition stiffness. We interpret the phenomenon that photoinduced hole doping, when surpassing a critical threshold value of ~ 10^(18)cm^(-3), sharply decreases the phase transition energy barrier. Our results provide new insights into controlling the CDW phase transition, paving the way for optical-controlled novel devices based on CDW materials.

Multi-modal knowledge graph inference via media convergence and logic rule

Media convergence works by processing information from different modalities and applying them to different domains.It is difficult for the conventional knowledge graph to utilise multi-media features because the introduction of a large amount of information from other modalities reduces the effectiveness of representation learning and makes knowledge graph inference less effective.To address the issue,an inference method based on Media Convergence and Rule-guided Joint Inference model(MCRJI)has been pro-posed.The authors not only converge multi-media features of entities but also introduce logic rules to improve the accuracy and interpretability of link prediction.First,a multi-headed self-attention approach is used to obtain the attention of different media features of entities during semantic synthesis.Second,logic rules of different lengths are mined from knowledge graph to learn new entity representations.Finally,knowledge graph inference is performed based on representing entities that converge multi-media features.Numerous experimental results show that MCRJI outperforms other advanced baselines in using multi-media features and knowledge graph inference,demonstrating that MCRJI provides an excellent approach for knowledge graph inference with converged multi-media features.

Unveiling the pressure-driven metal–semiconductor–metal transition in the doped TiS_(2)

Conventional theories expect that materials under pressure exhibit expanded valence and conduction bands,leading to increased electrical conductivity.Here,we report the electrical properties of the doped 1T-TiS_(2) under high pressure by electrical resistance investigations,synchrotron x-ray diffraction,Raman scattering and theoretical calculations.Up to 70 GPa,an unusual metal-semiconductor-metal transition occurs.Our first-principles calculations suggest that the observed anti-Wilson transition from metal to semiconductor at 17 GPa is due to the electron localization induced by the intercalated Ti atoms.This electron localization is attributed to the strengthened coupling between the doped Ti atoms and S atoms,and the Anderson localization arising from the disordered intercalation.At pressures exceeding 30.5 GPa,the doped TiS_(2) undergoes a re-metallization transition initiated by a crystal structure phase transition.We assign the most probable space group as P2_12_12_1.Our findings suggest that materials probably will eventually undergo the Wilson transition when subjected to sufficient pressure.

Ultrafast Carrier Dynamics in Ba_(6)Cr_(2)S_(10)Modified by Toroidal Magnetic Phase Transition

Ba_(6)Cr_(2)S_(10)is a recently discovered magnetic material,in which the spins are aligned ferromagnetically in the ab-plane and anti-parallelly in a paired form along the c-axis.It is characterized as a quasi-one dimensional(1D)dimerized structure with a ferrotoroidic order,forming the simplest candidate toroidal magnetic(TM)order and exhibiting an anti-ferromagnetic-like transition at around 10 K.Time-resolved ultrafast dynamics investigation of the novel A-Cr-S(A:metal elements)family of quantum materials has rarely been reported.Here,we investigate the time-resolved pump-probe ultrafast dynamics of a Ba6Cr2S10 single crystal.A prominent change in the photo-excited carrier dynamics is observed at T_(c)=10 K,corresponding to the reported TM-paramagnetic phase transition.A potential unknown magnetic transition is also found at T^(*)=29 K.Our results provide new evidence for the TM magnetic transition in Ba_(6)Cr_(2)S_(10),and shed light on phase transitions in TM quantum materials.

Publisher Correction to:Strongly Coupled 2D Transition Metal Chalcogenide‑MXene‑Carbonaceous Nanoribbon Heterostructures with Ultrafast Ion Transport for Boosting Sodium/Potassium Ions Storage

Due to the technical fault,a wrong version of the paper was uploaded.The content of the article was not affected,but the layout of the article was affected.The original article has been corrected.

Advancing neuroscience through real-time processing of big data:Transition from open-loop to closed-loop paradigms

The brain functions as a closed-loop system that continuously generates behavior in response to the external environment and adjusts actions based on the outcomes.Traditional research methodologies in neuroscience,especially those employed in brain imaging experiments,have mainly adopted an open-loop paradigm(Grosenick et al.,2015).Functional neural circuits are analyzed offline and subsequently tested through manipulation of neuronal activities within specific regions or with genetic markers.By establishing a closed-loop research paradigm,functional ensembles can be detected and tested in real time with temporal sequences.These functional ensembles,rather than brain regions or genetically labeled neural populations,serve as fundamental units of neural networks,offering valuable insights into the dissection of neural circuits.The closed-loop research paradigm also enables the capture of high-dimensional activities of internal brain dynamics and precise elucidation of physiological processes such as learning,decision-making,and sleep.

Dissipation-Driven Superradiant Phase Transition of a Two-Dimensional Bose-Einstein Condensate in a Double Cavity

We study superradiant phase transitions in a hybrid system of a two-dimensional Bose–Einstein condensate of atoms and two cavities arranged with a tilt angle.By adjusting the loss rate of cavities,we map out the phase diagram of steady states within a mean field framework.It is found that when the loss rates of the two cavities are different,superradiant transitions may not occur at the same time in the two cavities.A first-order phase transition is observed between the states with only one cavity in superradiance and both in superradiance.In the case that both cavities are superradiant,a net photon current is observed flowing from the cavity with small decay rate to the one with large decay rate.The photon current shows a non-monotonic dependence on the loss rate difference,owing to the competition of photon number difference and cavity field phase difference.Our findings can be realized and detected in experiments.

Evolution of Superconducting-Transition Temperature with Superfluid Density and Conductivity in Pressurized Cuprate Superconductors

What factors fundamentally determine the value of superconducting transition temperature Tc in high temperature superconductors has been the subject of intense debate.Following the establishment of an empirical law known as Homes'law,there is a growing consensus in the community that the Tc value of the cuprate superconductors is closely linked to the superfluid density(ρ_(s))of its ground state and the conductivity(σ)of its normal state.However,all the data supporting this empirical law(ρ_(s)=AσT_(c))have been obtained from the ambientpressure superconductors.In this study,we present the first high-pressure results about the connection of the quantities of ρ_(s) and σ with T_(c),through the studies on the Bi_(1.74)Pb_(0.38)Sr_(1.88)CuO_(6+δ)and Bi_(2)Sr_(2)CaCu_(2)O_(8+δ),in which the value of their high-pressure resistivity(ρ=1/σ)is achieved by adopting our newly established method,while the quantity ofρs is extracted using Homes'law.We highlight that the Tc values are strongly linked to the joint response factors of magnetic field and electric field,i.e.,ρ_(s) and σ,respectively,implying that the physics determining T_(c) is governed by the intrinsic electromagnetic fields of the system.

Generative Multi-Modal Mutual Enhancement Video Semantic Communications

Recently,there have been significant advancements in the study of semantic communication in single-modal scenarios.However,the ability to process information in multi-modal environments remains limited.Inspired by the research and applications of natural language processing across different modalities,our goal is to accurately extract frame-level semantic information from videos and ultimately transmit high-quality videos.Specifically,we propose a deep learning-basedMulti-ModalMutual Enhancement Video Semantic Communication system,called M3E-VSC.Built upon a VectorQuantized Generative AdversarialNetwork(VQGAN),our systemaims to leverage mutual enhancement among different modalities by using text as the main carrier of transmission.With it,the semantic information can be extracted fromkey-frame images and audio of the video and performdifferential value to ensure that the extracted text conveys accurate semantic information with fewer bits,thus improving the capacity of the system.Furthermore,a multi-frame semantic detection module is designed to facilitate semantic transitions during video generation.Simulation results demonstrate that our proposed model maintains high robustness in complex noise environments,particularly in low signal-to-noise ratio conditions,significantly improving the accuracy and speed of semantic transmission in video communication by approximately 50 percent.

Pressure-induced magnetic phase and structural transition in SmSb_(2)

Motivated by the recent discovery of unconventional superconductivity around a magnetic quantum critical point in pressurized CeSb_(2),here we present a high-pressure study of an isostructural antiferromagnetic(AFM) SmSb_(2) through electrical transport and synchrotron x-ray diffraction measurements.At P_C~2.5 GPa,we found a pressure-induced magnetic phase transition accompanied by a Cmca→P4/nmm structural phase transition.In the pristine AFM phase below P_C,the AFM transition temperature of SmSb_(2) is insensitive to pressure;in the emergent magnetic phase above P_C,however,the magnetic critical temperature increases rapidly with increasing pressure.In addition,at ambient pressure,the magnetoresistivity(MR) of SmSb_(2) increases suddenly upon cooling below the AFM transition temperature and presents linear nonsaturating behavior under high field at 2 K.With increasing pressure above P_C,the MR behavior remains similar to that observed at ambient pressure,both in terms of temperature-and field-dependent MR.This leads us to argue an AFM-like state for SmSb_(2) above P_C.Within the investigated pressure of up to 45.3 GPa and the temperature of down to 1.8 K,we found no signature of superconductivity in SmSb_(2).

Significantly enhanced thermal stability of HMX by phase-transition lysozyme coating

A new robust bio-inspired route by using lysozyme aqueous solution for surface modification on 1,3,5,7-tetranitro-1,3,5,7-tetrazocane(HMX)was described in this paper.HMX crystals were coated by in situ phase transition of lysozyme(PTL)molecules.The HMX decorated by PTL was characterized by SEM,XRD,FTIR and XPS,demonstrating a dense core-shell coating layer.The coverage of lysozyme on HMX crystal was calculated by the ratio of sulfur content.The surface coverage increased from 60.5% to 93.5% when the content of PTL was changed from 0.5 wt% to 2.0 wt%,indicating efficient coating.The thermal stability of HMX was investigated by in situ XRD and DSC.The thermal phase transition temperature of HMX(β to δ phase)was delayed by 42℃ with 2.0 wt% PTL coating,which prevented HMX from thermal damage and sensitivity by the effect of PTL coating.After heating at 215℃,large cracks appeared in the naked HMX crystal,while the PTL coated HMX still maintained intact,with the impact energy of HMX dropped dramatically from 5 J to 2 J.However,the impact energy of HMX with 1.0 wt% and 2.0 wt% coating content(HMX@PTL-1.0 and HMX@PTL-2.0)was unchanged(5 J).Present results potentially enable large-scale fabrication of polymorphic energetic materials with outstanding thermal stability by novel lysozyme coating.

Temperature-sensing array using the metal-to-insulator transition of Nd_(x)Sm_(1-x)NiO_(3)

Rare-earth nickelates(RENiO_(3))show widely tunable metal-to-insulator transition(MIT)properties with ignorable variations in lattice constants and small latent heat across the critical temperature(TMIT).Particularly,it is worth noting that compared with the more commonly investigated vanadium oxides,the MIT of RENiO_(3)is less abrupt but usually across a wider range of temperatures.This sheds light on their alternative applications as negative temperature coefficient resistance(NTCR)thermistors with high sensitivity compared with the current NTCR thermistors,other than their expected use as critical temperature resistance thermistors.In this work,we demonstrate the NTCR thermistor functionality for using the adjustable MIT of Nd_(x)Sm_(1-x)NiO_(3)within 200–400 K,which displays larger magnitudes of NTCR(e.g.,more than 7%/K)that is unattainable in traditional NTCR thermistor materials.The temperature dependence of resistance(R–T)shows sharp variation during the MIT of Nd_(x)Sm_(1-x)NiO_(3)with no hysteresis via decreasing the Nd content(e.g.,x≤0.8),and such a R–T tendency can be linearized by introducing an optimum parallel resistor.The sensitive range of temperature can be further extended to 210–360 K by combining a series of Nd_(x)Sm_(1-x)NiO_(3)with eight rare-earth co-occupation ratios as an array,with a high magnitude of NTCR(e.g.,7%–14%/K)covering the entire range of temperatures.

Metal-to-insulator transitions in 3d-band correlated oxides containing Fe compositions

Metal-to-insulator transitions (MITs),which are achieved in 3d-band correlated transitional metal oxides,trigger abrupt variations in electrical,optical,and/or magnetic properties beyond those of conventional semiconductors.Among such material families,iron(Fe:3d^(6)4s^(2))-containing oxides pique interest owing to their widely tunable MIT properties,which are associated with the various valence states of Fe.Their potential electronic applications also show promise,given the large abundance of Fe on Earth.Representative MIT properties triggered by critical temperature (TMIT) were reported for ReFe_(2)O_(4)(Fe^(2.5+)),ReBaFe_(2)O_(5)(Fe^(2.5+)),Fe_(3)O_(4)(Fe^(2.67+)),Re_(1/3)Sr_(2/3)FeO_(3)(Fe^(3.67+)),Re Cu_(3)Fe_(4)O_(12)(Fe^(3.75+)),and Ca_(1-x)Sr_(x)FeO_(3)(Fe^(4+))(where Re represents rare-earth elements).The common feature of MITs of these Fe-containing oxides is that they are usually accompanied by charge ordering transitions or disproportionation associated with the valence states of Fe.Herein,we review the material family of Fe-containing MIT oxides,their MIT functionalities,and their respective mechanisms.From the perspective of potentially correlated electronic applications,the tunability of the TMITand its resultant resistive change in Fe-containing oxides are summarized and further compared with those of other materials exhibiting MIT functionality.In particular,we highlight the abrupt MIT and wide tunability of TMITof Fe-containing quadruple perovskites,such as Re Cu3Fe4O12.However,their effective material synthesis still needs to be further explored to cater to potential applications.