Marketing research for a Nordland fishing company in the Chinese market

Masteroppgave i bedriftsøkonomi - Høgskolen i Bodø, 201

Deep Learning Based on Orthogonal Approximate Message Passing for CP-Free OFDM

Channel estimation and signal detection are very challenging for an orthogonal frequency division multiplexing (OFDM) system without cyclic prefix (CP). In this article, deep learning based on orthogonal approximate message passing (DL-OAMP) is used to address these problems. The DL-OAMP receiver includes a channel estimation neural network (CE-Net) and a signal detection neural network based on OAMP, called OAMP-Net. The CE-Net is initialized by the least square channel estimation algorithm and refined by minimum mean-squared error (MMSE) neural network. The OAMP-Net is established by unfolding the iterative OAMP algorithm and adding some trainable parameters to improve the detection performance. The DL-OAMP receiver is with low complexity and can estimate time-varying channels with only a single training. Simulation results demonstrate that the bit-error rate (BER) of the proposed scheme is lower than those of competitive algorithms for high-order modulation.Comment: 5 pages, 4 figures, updated manuscript, International Conference on Acoustics, Speech and Signal Processing (ICASSP 2019). arXiv admin note: substantial text overlap with arXiv:1903.0476

Developmental expression of a functional TASK-1 2P domain K+ channel in embryonic chick heart

<p>Abstract</p> <p>Background</p> <p>Background K⁺channels are the principal determinants of the resting membrane potential (RMP) in cardiac myocytes and thus, influence the magnitude and time course of the action potential (AP).</p> <p>Methods</p> <p>RT-PCR and <it>in situ </it>hybridization are used to study the distribution of TASK-1 and whole-cell patch clamp technique is employed to determine the functional expression of TASK-1 in embryonic chick heart.</p> <p>Results</p> <p>Chicken TASK-1 was expressed in the early tubular heart, then substantially decreased in the ventricles by embryonic day 5 (ED5), but remained relatively high in ED5 and ED11 atria. Unlike TASK-1, TASK-3 was uniformly expressed in heart at all developmental stages. <it>In situ </it>hybridization studies further revealed that TASK-1 was expressed throughout myocardium at Hamilton-Hamburger stages 11 and 18 (S11 & S18) heart. In ED11 heart, TASK-1 expression was more restricted to atria. Consistent with TASK-1 expression data, patch clamp studies indicated that there was little TASK-1 current, as measured by the difference currents between pH 8.4 and pH 7.4, in ED5 and ED11 ventricular myocytes. However, TASK-1 current was present in the early embryonic heart and ED11 atrial myocytes. TASK-1 currents were also identified as 3 μM anandamide-sensitive currents. 3 μM anandamide reduced TASK-1 currents by about 58% in ED11 atrial myocytes. Zn²⁺(100 μM) which selectively inhibits TASK-3 channel at this concentration had no effect on TASK currents. In ED11 ventricle where TASK-1 expression was down-regulated, I_K1was about 5 times greater than in ED11 atrial myocytes.</p> <p>Conclusion</p> <p>Functional TASK-1 channels are differentially expressed in the developing chick heart and TASK-1 channels contribute to background K⁺conductance in the early tubular embryonic heart and in atria. TASK-1 channels act as a contributor to background K⁺current to modulate the cardiac excitability in the embryonic heart that expresses little I_K1.</p

DePT: Decoupled Prompt Tuning

This work breaks through the Base-New Tradeoff (BNT)dilemma in prompt tuning, i.e., the better the tuned model generalizes to the base (or target) task, the worse it generalizes to new tasks, and vice versa. Specifically, through an in-depth analysis of the learned features of the base and new tasks, we observe that the BNT stems from a channel bias issue, i.e., the vast majority of feature channels are occupied by base-specific knowledge, resulting in the collapse of taskshared knowledge important to new tasks. To address this, we propose the Decoupled Prompt Tuning (DePT) framework, which decouples base-specific knowledge from feature channels into an isolated feature space during prompt tuning, so as to maximally preserve task-shared knowledge in the original feature space for achieving better zero-shot generalization on new tasks. Importantly, our DePT is orthogonal to existing prompt tuning methods, hence it can improve all of them. Extensive experiments on 11 datasets show the strong flexibility and effectiveness of DePT. Our code and pretrained models are available at https://github.com/Koorye/DePT.Comment: 13 page

Exploring Chinese students’ learning experience in CIC MOOC 2.0– A study with Chinese online communities

This research explores Chinese students’ learning experience in the Creativity, Innovation, and Change (CIC) Massive Open Online Course (MOOC) 2.0 from the cultural, language, and communication perspectives. The CIC MOOC was the first course offered in both English and Chinese in Coursera. Data in this study were collected via online survey, interviews, QQ chat logs, and discussion threads in Guokr platform. Content analysis was performed to identify key themes from the collected data. Findings reveal that differences exist in Eastern and Western societies regarding power distance, individualism versus collectivism, and masculinity versus femininity. Communication patterns also vary in QQ and Guokr online communities. In addition, Chinese students reported that translation helped them understand the course topics better, and the online interest group motivated them to participate in course activities and complete the course. The conclusions shed light on the design of future MOOCs, advocating for translating course content into different languages and building small online communities to meet learners’ needs and improve their learning experiences

DETA: Denoised Task Adaptation for Few-Shot Learning

Test-time task adaptation in few-shot learning aims to adapt a pre-trained task-agnostic model for capturing taskspecific knowledge of the test task, rely only on few-labeled support samples. Previous approaches generally focus on developing advanced algorithms to achieve the goal, while neglecting the inherent problems of the given support samples. In fact, with only a handful of samples available, the adverse effect of either the image noise (a.k.a. X-noise) or the label noise (a.k.a. Y-noise) from support samples can be severely amplified. To address this challenge, in this work we propose DEnoised Task Adaptation (DETA), a first, unified image- and label-denoising framework orthogonal to existing task adaptation approaches. Without extra supervision, DETA filters out task-irrelevant, noisy representations by taking advantage of both global visual information and local region details of support samples. On the challenging Meta-Dataset, DETA consistently improves the performance of a broad spectrum of baseline methods applied on various pre-trained models. Notably, by tackling the overlooked image noise in Meta-Dataset, DETA establishes new state-of-the-art results. Code is released at https://github.com/nobody-1617/DETA.Comment: 10 pages, 5 figure

Message Passing Meets Graph Neural Networks: A New Paradigm for Massive MIMO Systems

As one of the core technologies for 5G systems, massive multiple-input multiple-output (MIMO) introduces dramatic capacity improvements along with very high beamforming and spatial multiplexing gains. When developing efficient physical layer algorithms for massive MIMO systems, message passing is one promising candidate owing to the superior performance. However, as their computational complexity increases dramatically with the problem size, the state-of-the-art message passing algorithms cannot be directly applied to future 6G systems, where an exceedingly large number of antennas are expected to be deployed. To address this issue, we propose a model-driven deep learning (DL) framework, namely the AMP-GNN for massive MIMO transceiver design, by considering the low complexity of the AMP algorithm and adaptability of GNNs. Specifically, the structure of the AMP-GNN network is customized by unfolding the approximate message passing (AMP) algorithm and introducing a graph neural network (GNN) module into it. The permutation equivariance property of AMP-GNN is proved, which enables the AMP-GNN to learn more efficiently and to adapt to different numbers of users. We also reveal the underlying reason why GNNs improve the AMP algorithm from the perspective of expectation propagation, which motivates us to amalgamate various GNNs with different message passing algorithms. In the simulation, we take the massive MIMO detection to exemplify that the proposed AMP-GNN significantly improves the performance of the AMP detector, achieves comparable performance as the state-of-the-art DL-based MIMO detectors, and presents strong robustness to various mismatches.Comment: 30 Pages, 7 Figures, and 4 Tables. This paper has been submitted to the IEEE for possible publication. arXiv admin note: text overlap with arXiv:2205.1062