A Bootstrapping Method for Finer-Grained Opinion Mining Using Graph Model

PACLIC 23 / City University of Hong Kong / 3-5 December 200

Study on electromagnetically induced transparency effects in Dirac and VO2_2 hybrid material structure

In this paper, we present a metamaterial structure of Dirac and vanadium dioxide and investigate its optical properties using the finite-difference time-domain (FDTD) technique. Using the phase transition feature of vanadium dioxide, the design can realize active tuning of the PIT effect at terahertz frequency, thereby converting from a single PIT to a double PIT. When VO$_2$ is in the insulating state, the structure is symmetric to obtain a single-band PIT effect; When VO$_2$ is in the metallic state, the structure turns asymmetric to realize a dual-band PIT effect. This design provides a reference direction for the design of actively tunable metamaterials. Additionally, it is discovered that the transparent window's resonant frequency and the Dirac material's Fermi level in this structure have a somewhat linear relationship. In addition, the structure achieves superior refractive index sensitivity in the terahertz band, surpassing 1 THz/RIU. Consequently, the concept exhibits encouraging potential for application in refractive index sensors and optical switches

Bias-Aware Design for Informed Decisions: Raising Awareness of Self-Selection Bias in User Ratings and Reviews

People often take user ratings and reviews into consideration when shopping for products or services online. However, such user-generated data contains self-selection bias that could affect people decisions and it is hard to resolve this issue completely by algorithms. In this work, we propose to raise the awareness of the self-selection bias by making three types of information concerning user ratings and reviews transparent. We distill these three pieces of information (reviewers experience, the extremity of emotion, and reported aspects) from the definition of self-selection bias and exploration of related literature. We further conduct an online survey to assess the perceptions of the usefulness of such information and identify the exact facets people care about in their decision process. Then, we propose a visual design to make such details behind user reviews transparent and integrate the design into an experimental website for evaluation. The results of a between-subjects study demonstrate that our bias-aware design significantly increases the awareness of bias and their satisfaction with decision-making. We further offer a series of design implications for improving information transparency and awareness of bias in user-generated content

WC-SBERT: Zero-Shot Text Classification via SBERT with Self-Training for Wikipedia Categories

Our research focuses on solving the zero-shot text classification problem in NLP, with a particular emphasis on innovative self-training strategies. To achieve this objective, we propose a novel self-training strategy that uses labels rather than text for training, significantly reducing the model's training time. Specifically, we use categories from Wikipedia as our training set and leverage the SBERT pre-trained model to establish positive correlations between pairs of categories within the same text, facilitating associative training. For new test datasets, we have improved the original self-training approach, eliminating the need for prior training and testing data from each target dataset. Instead, we adopt Wikipedia as a unified training dataset to better approximate the zero-shot scenario. This modification allows for rapid fine-tuning and inference across different datasets, greatly reducing the time required for self-training. Our experimental results demonstrate that this method can adapt the model to the target dataset within minutes. Compared to other BERT-based transformer models, our approach significantly reduces the amount of training data by training only on labels, not the actual text, and greatly improves training efficiency by utilizing a unified training set. Additionally, our method achieves state-of-the-art results on both the Yahoo Topic and AG News datasets

Spindle oscillations are generated in the dorsal thalamus and modulated by the thalamic reticular nucleus

Spindle waves occur during the early stage of slow wave sleep and are thought to arise in the thalamic reticular nucleus (TRN), causing inhibitory postsynaptic potential spindle-like oscillations in the dorsal thalamus that are propagated to the cortex. We have found that thalamocortical neurons exhibit membrane oscillations that have spindle frequencies, consist of excitatory postsynaptic potentials, and co-occur with electroencephalographic spindles. TRN lesioning prolonged oscillations in the medial geniculate body (MGB) and auditory cortex (AC). Injection of GABA~A~ antagonist into the MGB decreased oscillation frequency, while injection of GABA~B~ antagonist increased spindle oscillations in the MGB and cortex. Thus, spindles originate in the dorsal thalamus and TRN inhibitory inputs modulate this process, with fast inhibition facilitating the internal frequency and slow inhibition limiting spindle occurrence