Electoral Accountability and Selection with Personalized Information Aggregation

We study a model of electoral accountability and selection (EAS) in which heterogeneous voters can aggregate the incumbent's performance data into personalized signals through paying limited attention. Extreme voters' signals exhibit an own-party bias, which hampers their abilities to discern good and bad performances. While this effect alone would undermine EAS, there is a countervailing effect stemming from partisan disagreements, which make the centrist voter pivotal and could potentially enhance EAS. Overall, increasing mass polarization and shrinking attention spans have ambiguous effects on EAS, whereas correlating voters' signals unambiguously improves EAS and voter welfare

A Study on the Input and Output of Vocabulary Teaching Based on Noticing Theory

As an important concept in cognitive psychology, noticing is pertinent to the daily life, working, and study. What is more, it is one of essential factors to have a bearing on language learning. When teachers input or output knowledge or material, not all the things can gain learners’ attention, that is, the value of these things is not equal. Based on the analysis of vocabulary from the textbook published by the People’s Education Publishing House from the perspective of Noticing Theory, this paper tries to explore some teaching cases of how to facilitate the input and output of vocabulary teaching, aiming to enhance learners’ efficiency, and provide some reference to teachers on how to attract learners’ attention. The result shows that it is better to design the tasks on the basis of those elements of Noticing Theory, that is, expectation or readiness, frequency, perceptual salience instruction, task demands, and skill level, which facilitates the vocabulary acquisition, and boosts learner’s interest and initiative to learn English vocabulary

Learning News Bias: Misspecifications and Consequences

We study how a decision maker (DM) learns about the bias of unfamiliar news sources. Absent any frictions, a rational DM uses known sources as a yardstick to discern the true bias of a source. If a DM has misspecified beliefs, this process fails. We derive long-run beliefs, behavior, welfare, and corresponding comparative statics, when the DM has dogmatic, incorrect beliefs about the bias of known sources. The distortion due to misspecified learning is succinctly captured by a single-dimensional metric we introduce. Our model generates the hostile media effect and false polarization, and has implications for fact-checking and misperception recalibration

A Rational Inattention Theory of Echo Chamber

Finite players allocate limited attention capacities across biased primary sources and other players in order to gather information about an uncertain state. The resulting Poisson attention network transmits information from primary sources to a player either directly or indirectly through the other players. We study when and why rational inattention leads players with similar preferences to form echo chambers, and why mandatorily exposing players to all biased sources could dissolve echo chambers but undermine welfare. We characterize the opinion distribution within an echo chamber, establishing the law of the few and the controversy of policy interventions that augment source visibility

Skeleton-OOD:An end-to-end skeleton-based model for robust out-of-distribution human action detection

Human action recognition is crucial in computer vision systems. However, in real-world scenarios, human actions often fall outside the distribution of training data, requiring a model to both recognize in-distribution (ID) actions and reject out-of-distribution (OOD) ones. Despite its importance, there has been limited research on OOD detection in human actions. Existing works on OOD detection mainly focus on image data with RGB structure, and many methods are post-hoc in nature. While these methods are convenient and computationally efficient, they often lack sufficient accuracy, fail to consider the exposure of OOD samples, and ignore the application in skeleton structure data. To address these challenges, we propose a novel end-to-end skeleton-based model called Skeleton-OOD, which is committed to improving the effectiveness of OOD tasks while ensuring the accuracy of ID recognition. Through extensive experiments conducted on NTU-RGB+D 60, NTU-RGB+D 120, and Kinetics-400 datasets, Skeleton-OOD demonstrates the superior performance of our proposed approach compared to state-of-the-art methods. Our findings underscore the effectiveness of classic OOD detection techniques in the context of skeleton-based action recognition tasks, offering promising avenues for future research in this field. Code is available at https://github.com/YilliaJing/Skeleton-OOD.git.</p

ICAR: Image-based Complementary Auto Reasoning

Scene-aware Complementary Item Retrieval (CIR) is a challenging task which requires to generate a set of compatible items across domains. Due to the subjectivity, it is difficult to set up a rigorous standard for both data collection and learning objectives. To address this challenging task, we propose a visual compatibility concept, composed of similarity (resembling in color, geometry, texture, and etc.) and complementarity (different items like table vs chair completing a group). Based on this notion, we propose a compatibility learning framework, a category-aware Flexible Bidirectional Transformer (FBT), for visual "scene-based set compatibility reasoning" with the cross-domain visual similarity input and auto-regressive complementary item generation. We introduce a "Flexible Bidirectional Transformer (FBT)" consisting of an encoder with flexible masking, a category prediction arm, and an auto-regressive visual embedding prediction arm. And the inputs for FBT are cross-domain visual similarity invariant embeddings, making this framework quite generalizable. Furthermore, our proposed FBT model learns the inter-object compatibility from a large set of scene images in a self-supervised way. Compared with the SOTA methods, this approach achieves up to 5.3% and 9.6% in FITB score and 22.3% and 31.8% SFID improvement on fashion and furniture, respectively

Metformin alleviates hepatic iron overload and ferroptosis through AMPK-ferroportin pathway in HFD-induced NAFLD

Highlights Metformin alleviates HIO and ferroptosis in HFD-induced NAFLD FPN is involved in the molecular mechanism of metformin on HIO in HFD-induced NAFLD Metformin upregulates FPN expression by reducing lysosomal ubiquitination degradation Summary Metformin prevents progression of non-alcoholic fatty liver disease (NAFLD). However, the potential mechanism is not entirely understood. Ferroptosis, a recently recognized nonapoptotic form of regulated cell death, has been reported to be involved in the pathogenesis of NAFLD. Here, we investigated the effects of metformin on ferroptosis and its potential mechanism in NAFLD. We found that metformin prevented the progression of NAFLD, and alleviated hepatic iron overload (HIO), ferroptosis and upregulated ferroportin (FPN) expression in vivo and in vitro. Mechanically, metformin reduced the lysosomal degradation pathway of FPN through activation AMPK, thus upregulated the expression of FPN protein, alleviated HIO and ferroptosis, and prevented progression of NAFLD. These findings discover a mechanism of metformin, suggesting that targeting FPN may have the therapeutic potential for treating NAFLD and related disorders

Hyperbolic Space with Hierarchical Margin Boosts Fine-Grained Learning from Coarse Labels

Learning fine-grained embeddings from coarse labels is a challenging task due to limited label granularity supervision, i.e., lacking the detailed distinctions required for fine-grained tasks. The task becomes even more demanding when attempting few-shot fine-grained recognition, which holds practical significance in various applications. To address these challenges, we propose a novel method that embeds visual embeddings into a hyperbolic space and enhances their discriminative ability with a hierarchical cosine margins manner. Specifically, the hyperbolic space offers distinct advantages, including the ability to capture hierarchical relationships and increased expressive power, which favors modeling fine-grained objects. Based on the hyperbolic space, we further enforce relatively large/small similarity margins between coarse/fine classes, respectively, yielding the so-called hierarchical cosine margins manner. While enforcing similarity margins in the regular Euclidean space has become popular for deep embedding learning, applying it to the hyperbolic space is non-trivial and validating the benefit for coarse-to-fine generalization is valuable. Extensive experiments conducted on five benchmark datasets showcase the effectiveness of our proposed method, yielding state-of-the-art results surpassing competing methods.Comment: Accepted by NeurIPS 202