Hybrid deliberation: Citizen dialogues in a post-pandemic era

This report first provides a brief review of various forms of dialogue-based participation, e.g., Citizen Assembly, Citizen Lottery, Citizen Jury, Deliberative Polling, and Participatory Budgeting. Challenges associated with these long-lasting practices are identified and hybrid deliberation is proposed as a concept to address the challenges. The report then analyzes six leading examples of digital or hybrid formats of citizen dialogues. Through the comparison of the cases, the report concludes about the hurdles/risks, success factors/opportunities, and best practices for a complementary use of digital and analogue participation formats. Hybrid deliberation is proposed to be the future direction for dialogue-based participation that involves masses and generates high-quality outcomes

Precise Phase Transition of Total Variation Minimization

Characterizing the phase transitions of convex optimizations in recovering structured signals or data is of central importance in compressed sensing, machine learning and statistics. The phase transitions of many convex optimization signal recovery methods such as $\ell_1$ minimization and nuclear norm minimization are well understood through recent years' research. However, rigorously characterizing the phase transition of total variation (TV) minimization in recovering sparse-gradient signal is still open. In this paper, we fully characterize the phase transition curve of the TV minimization. Our proof builds on Donoho, Johnstone and Montanari's conjectured phase transition curve for the TV approximate message passing algorithm (AMP), together with the linkage between the minmax Mean Square Error of a denoising problem and the high-dimensional convex geometry for TV minimization.Comment: 6 page

The construction of authorial voice in writing research articles: A corpus-based study from an APPRAISAL theory perspective

This study explores voice from an APPRAISAL theory perspective. It aims to investigate how published research writers deploy ATTITUDE and GRADUATION resources to review existing literature in the field. The study is based on a corpus of literature reviews (LRs) from 204 research articles (RAs) in computer networks and communications (CNC) and second language writing (SLW). Findings show that 1) writers demonstrate a strong preference to express their attitude through APPRECIATION rather than AFFECT and JUDGEMENT resources; 2) more FORCE than FOCUS resources are used to upgrade attitudinal meanings realized through ATTITUDE resources or to evoke APPRECIATION; and 3) one-way ANOVA and post hoc tests have detected significant differences in the use of AFFECT and JUDGEMENT resources and in two sub-categories of FORCE and FOCUS resources. The study contributes to new knowledge by relating ATTITUDE and GRADUATION resources to the construction of voice in the disciplines of CNC and SLW.&nbsp

Examining the Impact of Source-product Congruence and Sponsorship Disclosure on the Communicative Effectiveness of Instagram Influencers

Guided by the Persuasion Knowledge Model and the Attribution Theory, this study investigates the perceived source expertise-product attribute congruence and sponsorship disclosure as pertinent factors affecting the communicative effectiveness of influencers. Instagram, with an immense influencer market value projected at USD2.3 billion in 2020, was chosen as the platform context. The study utilised a 2 (source expertise) x2 (product category) x2 (sponsorship disclosure) experiment to examine the roles of source-product congruence and sponsorship disclosure in affecting consumers' perception of extrinsic and intrinsic source motives, consumer resistance and ultimately, advertising effectiveness. Results revealed that the presence of a sponsorship disclosure generated stronger perceptions of extrinsic source motives but did not impact consumer resistance and advertising effectiveness, indicating that the activation of consumers' conceptual persuasion knowledge may not necessarily affect attitudinal persuasion knowledge. Source-product congruence, on the other hand, had main impacts on intrinsic motives, consumer resistance and ad effectiveness. In addition, hierarchical multiple regressions found that source-product congruence triggers a multi-stage process where consumers' perception of intrinsic source motives mediates consumer resistance which subsequently, mediates the relationship between source-product congruence and ad effectiveness

Mechanical behavior of irregular fibers part II : non-linear tensile behavior

To further our study of the linear tensile behavior of irregular fibers, in this paper we examine the nonlinear tensile behavior of irregular fibers. As before, we simulate the fiber dimensional irregularities with sine waves of different magnitude and frequency, and report results on the tensile behavior and gauge length effect of the simulated fibers. <br /

Discrimintive Image Warping with Attribute Flow

We address the problem of finding deformation between two images for the purpose of recognizing objects. The challenge is that discriminative features are often transformation-variant (e.g. histogram of oriented gradients, texture), while transformation-invariant features (e.g. intensity, color) are often not discriminative. We introduce the concept of attribute flow which explicitly models how image attributes vary with its deformation. We develop a non-parametric method to approximate this using histogram matching, which can be solved efficiently using linear programming. Our method produces dense correspondence between images, and utilizes discriminative, transformation-variant features for simultaneous detection and alignment. Experiments on ETHZ shape categories dataset show that we can accurately recognize highly deformable objects with few training examples

Tackling the Non-IID Issue in Heterogeneous Federated Learning by Gradient Harmonization

Federated learning (FL) is a privacy-preserving paradigm for collaboratively training a global model from decentralized clients. However, the performance of FL is hindered by non-independent and identically distributed (non-IID) data and device heterogeneity. In this work, we revisit this key challenge through the lens of gradient conflicts on the server side. Specifically, we first investigate the gradient conflict phenomenon among multiple clients and reveal that stronger heterogeneity leads to more severe gradient conflicts. To tackle this issue, we propose FedGH, a simple yet effective method that mitigates local drifts through Gradient Harmonization. This technique projects one gradient vector onto the orthogonal plane of the other within conflicting client pairs. Extensive experiments demonstrate that FedGH consistently enhances multiple state-of-the-art FL baselines across diverse benchmarks and non-IID scenarios. Notably, FedGH yields more significant improvements in scenarios with stronger heterogeneity. As a plug-and-play module, FedGH can be seamlessly integrated into any FL framework without requiring hyperparameter tuning

Mechanical behavior of irregular fibers part I : modeling the tensile behavior of linear elastic fibers

Fiber irregularities are inherent to textile fibers, natural fibers in particular. This series of papers examines the impact of fiber irregularity on the mechanical behavior of textile fibers. In the first part, the effect of fiber dimensional irregularities on the tensile behavior of linear elastic fibers is examined, using the finite element method (FEM). Fiber dimensional irregularities are simulated with sine waves of different magnitude and frequency. The results indicate that increasing the level or magnitude of irregularity will decrease the breaking load, breaking elongation and method Young&rsquo;s modulus of the fiber, while increasing the frequency of irregularity will decrease the breaking load and method Young&rsquo;s modulus, but the breaking elongation will increase. Fiber dimensional irregularity and the gauge length effect are also simulated in this study.<br /