查看更多>>摘要：By a News Reporter-Staff News Editor at Network Daily News - A new studyon Networks is now available. According to news reporting originating from Jinan, People’s Republic ofChina, by NewsRx correspondents, research stated, “Ground-penetrating radar (GPR) with different centerfrequencies can detect defects at different depths with a range of resolutions enabling it to be used forsubsurface defect inspection. However, the existing deep learning methods cannot accurately invert thepermittivity from GPR data of different frequencies, due to the limited number of labeled GPR images forevery center frequency.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Network Daily News - Investigatorspublish new report on Networks. According to news reporting from Columbia, South Carolina, by NewsRxeditors, the research stated, “In this study, we propose an over-the-air computation (OAC) scheme tocalculate the majority vote (MV) for federated edge learning (FEEL). With the proposed approach, edgedevices (EDs) transmit the signs of local stochastic gradients, i.e., votes, by activating one of two orthogonalresources.”The news correspondents obtained a quote from the research from the University of South Carolina,“The MVs at the edge server (ES) are obtained with non-coherent detectors by exploiting the accumulationson the resources. Hence, the proposed scheme eliminates the need for channel state information (CSI)at the EDs and ES. In this study, we analyze various gradient-encoding strategies through the weightfunctions and waveform configurations over orthogonal frequency division multiplexing (OFDM). We showthat specific weight functions that enable absentee EDs (i.e., hard-coded participation with absentees(HPA)) or weighted votes (i.e., soft-coded participation (SP)) can substantially reduce the probability ofdetecting the incorrect MV. By taking path loss, power control, cell size, and fading channel into account,we prove the convergence of the distributed learning for a non-convex function for HPA.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Network Daily News - Fresh data onNetworks are presented in a new report. According to news reporting originating from Bergen, Norway, byNewsRx correspondents, research stated, “Road safety is tackled and an intelligent deep learning frameworkis proposed in this work, which includes outlier detection, vehicle detection, and accident estimation. Theroad state is first collected, while an intelligent filter, based on SIFT extractor and a Chinese restaurantprocess is used to remove noise.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Network Daily News - Data detailedon Machine Learning have been presented. According to news reporting out of Topi, Pakistan, by NewsRxeditors, research stated, “Emotion detection systems play a crucial role in enhancing human-computerinteraction. Existing systems predominantly rely on machine learning techniques.”Financial support for this research came from GIK Institute graduate program research fund.Our news journalists obtained a quote from the research from the Ghulam Ishaq Khan Institute of EngineeringScience & Technology, “This study introduces a novel emotion detection method that employsdeep learning techniques to identify five basic human emotions and the pleasure dimensions (valence) associatedwith these emotions, using text and keystroke dynamics. To facilitate this, we develop a non-acteddataset, DEKT-345 x 2, which includes text and keystroke features. The dataset is created by inducingemotions in participants under controlled conditions. Deep learning models are subsequently employed topredict a person’s affective state using textual content. Semantic analysis of the text data is achieved byemploying the global vector (Glove) representation of words. For both text and keystroke-based analysis,one-dimensional convolutional neural network (Conv1D), long short-term memory (LSTM), sandwichConv1D, and sandwich LSTM models are employed. The robustness of our proposed method is assessedusing the DEKT-345 x 2 dataset, which collects text and keystroke information from 69 participants.Through parameter tuning on training and validation data, we establish models that demonstrate superiorperformance compared to five related approaches and three machine learning classifiers.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Network Daily News - Current studyresults on artificial neural networks have been published. According to news reporting originating fromIslamic Azad University by NewsRx correspondents, research stated, “Due to the exponential rise of ICTtechnologies, the digital banking industry has made tremendous advancements in user-friendly, effective,and quick financial transactions. Numerous new banking services, products, and business opportunitieshave resulted as a result.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Network Daily News - Current studyresults on Networks have been published. According to news reporting from Changzhou, People’s Republicof China, by NewsRx journalists, research stated, “This paper studies a cascade tri-neuron Hopfield neuralnetwork (CTN-HNN) with no connection between the first neuron and the third neuron. Such incompletelyconnected neuronal structure may be part of complex neural networks with negligible inputs, and may bepresent in abnormal neural networks with some neurological diseases.”Financial supporters for this research include National Natural Science Foundation of China (NSFC),Qinglan Project of Jiangsu Province of China, Jiangsu Government Scholarship for Overseas Studies,Postgraduate Research & Practice Innovation Program of Jiangsu Province of China, Natural ScienceFoundation of Henan.
查看更多>>摘要：By a News Reporter-Staff News Editor at Network Daily News - Investigatorspublish new report on Networks. According to news reporting originating from Kjeller, Norway, by NewsRxcorrespondents, research stated, “Array processing is an integral part of automatic seismic event detectionpipelines for measuring apparent velocity and backazimuth of seismic arrivals. Both quantities are usuallymeasured under the plane-wave assumption, and are essential to classify the phase type and to determinethe direction toward the event epicenter.”Our news editors obtained a quote from the research from NORSAR, “However, structural inhomogeneitiescan lead to deviations from the plane-wave model, which must be taken into account for phaseclassification and back-azimuth estimation. We suggest a combined classification and regression neural network,which we call ArrayNet, to determine the phase type and back -azimuth directly from the arrival-timedifferences between all combinations of stations of a given seismic array without assuming a plane-wavemodel. ArrayNet is trained using regional P-and S -wave arrivals of over 30,000 seismic events from reviewedregional bulletins in northern Europe from the past three decades. ArrayNet models are generatedand trained for each of the ARCES, FINES, and SPITS seismic arrays. We observe excellent per-formancefor the seismic phase classification (up to 99% accuracy), and the derived back -azimuth residuals aresignificantly improved in comparison with traditional array processing results using the plane-wave assumption.The SPITS array in Svalbard exhibits particular issues when it comes to array processing in the formof high apparent seismic velocities and a multitude of frost quake signals inside the array, and we showhow our new approach better handles these obstacles. Furthermore, we demonstrate the performance ofArrayNet on 20 months of continuous phase detections from the ARCES array and investigate the resultsfor a selection of regional seismic events of interest.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Network Daily News - A new study onapplied network science is now available. According to news reporting originating from the National Centerfor Scientific Research (CNRS) by NewsRx correspondents, research stated, “A growing number of socialmedia studies in the U.S. rely on the characterization of the opinion of individual users, for example, asDemocrat- or Republican-leaning, or in continuous scales ranging from most liberal to most conservative.”Funders for this research include Agence Nationale De La Recherche; Horizon 2020.The news editors obtained a quote from the research from National Center for Scientific Research(CNRS): “Recent works have shown, however, that additional opinion dimensions, for instance measuringattitudes towards elites, institutions, or cultural change, are also relevant for understanding socioinformationalphenomena on social platforms and in politics in general. The study of social networks inhigh-dimensional opinion spaces remains challenging in the US, both because of the relative dominance ofa principal liberal-conservative dimension in observed phenomena, and because two-party political systemsstructure both the preferences of users and the tools to measure them. This article leverages graph embeddingin multi-dimensional latent opinion spaces and text analysis to propose a method to identify additionalopinion dimensions linked to cultural, policy, social, and ideological groups and preferences. Using Twittersocial graph data we infer the political stance of nearly 2 million users connected to the political debate inthe U.S. for several issue dimensions of public debate. We show that it is possible to identify several newdimensions structuring social graphs, non-aligned with the classic liberal-conservative dimension. We alsoshow how the social graph is polarized to different degrees along these newfound dimensions, leveragingmulti-modality measures in opinion space.”
查看更多>>摘要：By a News Reporter-Staff News Editor at Network Daily News - According tonews reporting based on a preprint abstract, our journalists obtained the following quote sourced frombiorxiv.org:“The pulsatile activity of gonadotropin-releasing hormone neurons (GnRH neurons) is a key factor inthe regulation of reproductive hormones.“This pulsatility is orchestrated by a network of neurons that release the neurotransmitters kisspeptin,neurokinin B, and dynorphin (KNDy neurons), and produce episodic bursts of activity driving the GnRHneurons. We show in this computational study that the features of coordinated KNDy neuron activity canbe explained by a neural network in which connectivity among neurons is modular.
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