An Arnoldi-frontal approach for the stability analysis of flows in a collapsible channel

In this paper, we present a new approach based on a combination of the Arnoldi and frontal methods for solving large sparse asymmetric and generalized complex eigenvalue problems. The new eigensolver seeks the most unstable eigensolution in the Krylov subspace and makes use of the efficiency of the frontal solver developed for the finite element methods. The approach is used for a stability analysis of flows in a collapsible channel and is found to significantly improve the computational efficiency compared to the traditionally used QZ solver or a standard Arnoldi method. With the new approach, we are able to validate the previous results obtained either on a much coarser mesh or estimated from unsteady simulations. New neutral stability solutions of the system have been obtained which are beyond the limits of previously used methods

Reward-based Crowdfunding Success Prediction with Multimodal Data

As an increasing number of crowdfunding platforms recommend that entrepreneurs post multimodal data to improve data diversity and attract investors’ attention, it becomes necessary to study how functions of multimodal data take effect to predict fundraising outcomes (i.e., success or failure). There is a lack of research providing a comprehensive investigation of multimodal data in crowdfunding. Rooted in language and visual image metafunctional theories, we propose a framework to explore ideational, interpersonal, and textual metafunctions of multimodal data. We empirically examine the effectiveness of each metafunction, each modality, and their combination in predicting fundraising outcomes. The empirical evaluation shows the predictive utility of any metafunctions and metafunction combinations. The results also demonstrate that adding data modalities can help to improve the prediction performance

Enabling Feedback-Free MIMO Transmission for FD-RAN: A Data-driven Approach

To enhance flexibility and facilitate resource cooperation, a novel fully-decoupled radio access network (FD-RAN) architecture is proposed for 6G. However, the decoupling of uplink (UL) and downlink (DL) in FD-RAN makes the existing feedback mechanism ineffective. To this end, we propose an end-to-end data-driven MIMO solution without the conventional channel feedback procedure. Data-driven MIMO can alleviate the drawbacks of feedback including overheads and delay, and can provide customized precoding design for different BSs based on their historical channel data. It essentially learns a mapping from geolocation to MIMO transmission parameters. We first present a codebook-based approach, which selects transmission parameters from the statistics of discrete channel state information (CSI) values and utilizes integer interpolation for spatial inference. We further present a non-codebook-based approach, which 1) derives the optimal precoder from the singular value decomposition (SVD) of the channel; 2) utilizes variational autoencoder (VAE) to select the representative precoder from the latent Gaussian representations; and 3) exploits Gaussian process regression (GPR) to predict unknown precoders in the space domain. Extensive simulations are performed on a link-level 5G simulator using realistic ray-tracing channel data. The results demonstrate the effectiveness of data-driven MIMO, showcasing its potential for application in FD-RAN and 6G

MiR-489 serves as a tumor inhibitor in pituitary prolactinoma targeting p21-activated kinase 3

Purpose: To evaluate the effect of microRNA-489 (miR-489) on pituitary prolactinoma and its mechanisms of action. Methods: MMQ and GH3 cells were transfected with miR-489, cell viability assessed with cell counting kit-8 (CCK-8), and clone spots was evaluated by colony formation assay. Transwell assay was applied to measure cell migration and invasion while TargetScan was employed to the presumed targets of miR-489, followed by luciferase reporter assays. was MiR-489 and p21-activated kinase 3 (PAK3) gene expression were determined by quantitative reverse transcription-polymerase chain reaction (qRT-PCR. Protein levels of PAK3 were measured using western blots. Results: Transfection significantly increased miRNA-489 levels (p < 0.01). Cell viability, number of clone spots, as well as cell migration and invasion diminished in MMQ and GH3 cells following miR-489 transfection when compared to miR-NC mimic group (p < 0.01). The presumed binding site of miRNA- 489 was located in 3′-untranslated region (UTR) of PAK3, and miR-489 transfection repressed luciferase activity with the wild-type 3′-UTR (p < 0.05). In addition, miR-489 decreased PAK3 levels in MMQ and GH3 cells. Knockdown of PAK3 significantly suppressed cell viability, clone formation ability, as well as cell migration and invasion when compared to negative control (p < 0.01). Conclusion: MiR-489 overexpression suppresses pituitary prolactinoma by targeting PAK3, thus providing a potential therapeutic strategy for the management of pituitary prolactinoma

Recognizing Conditional Causal Relationships about Emotions and Their Corresponding Conditions

The study of causal relationships between emotions and causes in texts has recently received much attention. Most works focus on extracting causally related clauses from documents. However, none of these works has considered that the causal relationships among the extracted emotion and cause clauses can only be valid under some specific context clauses. To highlight the context in such special causal relationships, we propose a new task to determine whether or not an input pair of emotion and cause has a valid causal relationship under different contexts and extract the specific context clauses that participate in the causal relationship. Since the task is new for which no existing dataset is available, we conduct manual annotation on a benchmark dataset to obtain the labels for our tasks and the annotations of each context clause's type that can also be used in some other applications. We adopt negative sampling to construct the final dataset to balance the number of documents with and without causal relationships. Based on the constructed dataset, we propose an end-to-end multi-task framework, where we design two novel and general modules to handle the two goals of our task. Specifically, we propose a context masking module to extract the context clauses participating in the causal relationships. We propose a prediction aggregation module to fine-tune the prediction results according to whether the input emotion and causes depend on specific context clauses. Results of extensive comparative experiments and ablation studies demonstrate the effectiveness and generality of our proposed framework

A Wide-Band Test Fixture for Analyzing Parasitic Effects of RF Passive SMD Components

A wide-band test fixture is designed for the measurement of parasitic effects of RF passive SMD (surface mounted devices) components. Two calibration methods, TRM (Thru-Reflect-Match) from 45 MHz to 2 GHz and TRL (Thru-Reflect-Line) from 2 GHz to 12 GHz, are used for error correction. The measurement standards and fixture are designed based on these two calibration methods. For experimental verification, the multilayered ceramic SMD capacitors of Johanson Technology are measured. The parasitic effects of the SMD capacitors are analyzed. The designed fixture is feasible and applicable for quick and accurate measurement of RF passive SMD components