The Existence of Social Movement Organization(SMO) &Comparison of Collective and Connective Action in the Digital Era ---- an Analysis of 15-M Movement in Spain

Social movement organizations (SMOs) have been performing a significant role in terms of gathering like-minded civil individuals with common interests during social movements. Stepping into the digital era, the social media becomes prevailing in transforming people’s lifestyles. This essay will discuss the 15-M Movement in Spain to explore the transition of SMO’s position from conventional social movements to those in the digital era in the light of collective action logic and connective action logic. With the phenomenon that SMO itself sometimes is the original source of problems to trigger social movements, it is reasonable to see the decreasingly important SMO with the successful example of the 15-M Movement to engage over 60 cities in Spain and avoid the “free ride” problem via completely excluding brick and mortar organizations

On the Mechanics of NFT Valuation: AI Ethics and Social Media

As CryptoPunks pioneers the innovation of non-fungible tokens (NFTs) in AI and art, the valuation mechanics of NFTs has become a trending topic. Earlier research identifies the impact of ethics and society on the price prediction of CryptoPunks. Since the booming year of the NFT market in 2021, the discussion of CryptoPunks has propagated on social media. Still, existing literature hasn't considered the social sentiment factors after the historical turning point on NFT valuation. In this paper, we study how sentiments in social media, together with gender and skin tone, contribute to NFT valuations by an empirical analysis of social media, blockchain, and crypto exchange data. We evidence social sentiments as a significant contributor to the price prediction of CryptoPunks. Furthermore, we document structure changes in the valuation mechanics before and after 2021. Although people's attitudes towards Cryptopunks are primarily positive, our findings reflect imbalances in transaction activities and pricing based on gender and skin tone. Our result is consistent and robust, controlling for the rarity of an NFT based on the set of human-readable attributes, including gender and skin tone. Our research contributes to the interdisciplinary study at the intersection of AI, Ethics, and Society, focusing on the ecosystem of decentralized AI or blockchain. We provide our data and code for replicability as open access on GitHub.Comment: Presented at ChainScience Conference, 2003 (arXiv:2307.03277v2 [cs.DC] 11 Jul 2023

Joint prediction of travel mode choice and purpose from travel surveys: A multitask deep learning approach

The prediction and behavioural analysis of travel mode choice and purpose are critical for transport planning and have attracted increasing interest in research. Traditionally, the prediction of travel mode choice and trip purpose has been tackled separately, which fail to fully leverage the shared information between travel mode and purpose. This study addresses this gap by proposing a multitask learning deep neural network framework (MTLDNN) to jointly predict mode choice and purpose. We empirically evaluate and validate this framework using the household travel survey data in Greater London, UK. The results show that this framework has significantly lower cross-entropy loss than multinomial logit models (MNL) and single-task-learning deep neural network models (STLDNN). On the other hand, the predictive accuracy of MTLDNN is similar to STLDNN and is significantly higher than MNL. Moreover, in terms of behaviour analysis, the substitution pattern and choice probability of MTLDNN regarding input variables largely agree with MNL and STLDNN. This work demonstrates that MTLDNN is efficient in utilising the information shared by travel mode choice and purpose, and is capable of producing behaviourally reasonable substitution patterns across travel modes. Future research would develop more advanced MTLDNN frameworks for travel behaviour analysis and generalise MTLDNN to other travel behaviour topics

Blockchain Network Analysis: A Comparative Study of Decentralized Banks

Decentralized finance (DeFi) is known for its unique mechanism design, which applies smart contracts to facilitate peer-to-peer transactions. The decentralized bank is a typical DeFi application. Ideally, a decentralized bank should be decentralized in the transaction. However, many recent studies have found that decentralized banks have not achieved a significant degree of decentralization. This research conducts a comparative study among mainstream decentralized banks. We apply core-periphery network features analysis using the transaction data from four decentralized banks, Liquity, Aave, MakerDao, and Compound. We extract six features and compare the banks' levels of decentralization cross-sectionally. According to the analysis results, we find that: 1) MakerDao and Compound are more decentralized in the transactions than Aave and Liquity. 2) Although decentralized banking transactions are supposed to be decentralized, the data show that four banks have primary external transaction core addresses such as Huobi, Coinbase, Binance, etc. We also discuss four design features that might affect network decentralization. Our research contributes to the literature at the interface of decentralized finance, financial technology (Fintech), and social network analysis and inspires future protocol designs to live up to the promise of decentralized finance for a truly peer-to-peer transaction network

How to Define the Propagation Environment Semantics and Its Application in Scatterer-Based Beam Prediction

In view of the propagation environment directly determining the channel fading, the application tasks can also be solved with the aid of the environment information. Inspired by task-oriented semantic communication and machine learning (ML) powered environment-channel mapping methods, this work aims to provide a new view of the environment from the semantic level, which defines the propagation environment semantics (PES) as a limited set of propagation environment semantic symbols (PESS) for diverse application tasks. The PESS is extracted oriented to the tasks with channel properties as a foundation. For method validation, the PES-aided beam prediction (PESaBP) is presented in non-line-of-sight (NLOS). The PESS of environment features and graphs are given for the semantic actions of channel quality evaluation and target scatterer detection of maximum power, which can obtain 0.92 and 0.9 precision, respectively, and save over 87% of time cost.Comment: 5 pages, 5 figure

A note on additive complements of the squares

Let $\mathcal{S}=\{1^2,2^2,3^2,...\}$ be the set of squares and $\mathcal{W}=\{w_n\}_{n=1}^{\infty} \subset \mathbb{N}$ be an additive complement of $\mathcal{S}$ so that $\mathcal{S} + \mathcal{W} \supset \{n \in \mathbb{N}: n \geq N_0\}$ for some $N_0$. Let $\mathcal{R}_{\mathcal{S},\mathcal{W}}(n) = \#\{(s,w):n=s+w, s\in \mathcal{S}, w\in \mathcal{W}\}$. In 2017, Chen-Fang \cite{C-F} studied the lower bound of $\sum_{n=1}^NR_{\mathcal{S},\mathcal{W}}(n)$. In this note, we improve Cheng-Fang's result and get that $$\sum_{n=1}^NR_{\mathcal{S},\mathcal{W}}(n)-N\gg N^{1/2}.$$ As an application, we make some progress on a problem of Ben Green problem by showing that $$\limsup_{n\rightarrow\infty}\frac{\frac{\pi^2}{16}n^2-w_n}{n}\ge \frac{\pi}{4}+\frac{0.193\pi^2}{8}.$$Comment: The new version significantly improves the result of the former on

Contextual Biasing of Named-Entities with Large Language Models

This paper studies contextual biasing with Large Language Models (LLMs), where during second-pass rescoring additional contextual information is provided to a LLM to boost Automatic Speech Recognition (ASR) performance. We propose to leverage prompts for a LLM without fine tuning during rescoring which incorporate a biasing list and few-shot examples to serve as additional information when calculating the score for the hypothesis. In addition to few-shot prompt learning, we propose multi-task training of the LLM to predict both the entity class and the next token. To improve the efficiency for contextual biasing and to avoid exceeding LLMs' maximum sequence lengths, we propose dynamic prompting, where we select the most likely class using the class tag prediction, and only use entities in this class as contexts for next token prediction. Word Error Rate (WER) evaluation is performed on i) an internal calling, messaging, and dictation dataset, and ii) the SLUE-Voxpopuli dataset. Results indicate that biasing lists and few-shot examples can achieve 17.8% and 9.6% relative improvement compared to first pass ASR, and that multi-task training and dynamic prompting can achieve 20.0% and 11.3% relative WER improvement, respectively.Comment: 5 pages, 4 figures. Conference: ICASSP 202

When STING meets viruses: Sensing, trafficking and response

To effectively defend against microbial pathogens, the host cells mount antiviral innate immune responses by producing interferons (IFNs), and hundreds of IFN-stimulated genes (ISGs). Upon recognition of cytoplasmic viral or bacterial DNAs and abnormal endogenous DNAs, the DNA sensor cGAS synthesizes 2\u27,3\u27-cGAMP that induces STING (stimulator of interferon genes) undergoing conformational changes, cellular trafficking, and the activation of downstream factors. Therefore, STING plays a pivotal role in preventing microbial pathogen infection by sensing DNAs during pathogen invasion. This review is dedicated to the recent advances in the dynamic regulations of STING activation, intracellular trafficking, and post-translational modifications (PTMs) by the host and microbial proteins