High reward enhances perceptual learning.

Studies of perceptual learning have revealed a great deal of plasticity in adult humans. In this study, we systematically investigated the effects and mechanisms of several forms (trial-by-trial, block, and session rewards) and levels (no, low, high, subliminal) of monetary reward on the rate, magnitude, and generalizability of perceptual learning. We found that high monetary reward can greatly promote the rate and boost the magnitude of learning and enhance performance in untrained spatial frequencies and eye without changing interocular, interlocation, and interdirection transfer indices. High reward per se made unique contributions to the enhanced learning through improved internal noise reduction. Furthermore, the effects of high reward on perceptual learning occurred in a range of perceptual tasks. The results may have major implications for the understanding of the nature of the learning rule in perceptual learning and for the use of reward to enhance perceptual learning in practical applications

Learning Granularity-Unified Representations for Text-to-Image Person Re-identification

Text-to-image person re-identification (ReID) aims to search for pedestrian images of an interested identity via textual descriptions. It is challenging due to both rich intra-modal variations and significant inter-modal gaps. Existing works usually ignore the difference in feature granularity between the two modalities, i.e., the visual features are usually fine-grained while textual features are coarse, which is mainly responsible for the large inter-modal gaps. In this paper, we propose an end-to-end framework based on transformers to learn granularity-unified representations for both modalities, denoted as LGUR. LGUR framework contains two modules: a Dictionary-based Granularity Alignment (DGA) module and a Prototype-based Granularity Unification (PGU) module. In DGA, in order to align the granularities of two modalities, we introduce a Multi-modality Shared Dictionary (MSD) to reconstruct both visual and textual features. Besides, DGA has two important factors, i.e., the cross-modality guidance and the foreground-centric reconstruction, to facilitate the optimization of MSD. In PGU, we adopt a set of shared and learnable prototypes as the queries to extract diverse and semantically aligned features for both modalities in the granularity-unified feature space, which further promotes the ReID performance. Comprehensive experiments show that our LGUR consistently outperforms state-of-the-arts by large margins on both CUHK-PEDES and ICFG-PEDES datasets. Code will be released at https://github.com/ZhiyinShao-H/LGUR.Comment: Accepted by ACM Multimedia 202

Discourse-Aware Graph Networks for Textual Logical Reasoning

Textual logical reasoning, especially question-answering (QA) tasks with logical reasoning, requires awareness of particular logical structures. The passage-level logical relations represent entailment or contradiction between propositional units (e.g., a concluding sentence). However, such structures are unexplored as current QA systems focus on entity-based relations. In this work, we propose logic structural-constraint modeling to solve the logical reasoning QA and introduce discourse-aware graph networks (DAGNs). The networks first construct logic graphs leveraging in-line discourse connectives and generic logic theories, then learn logic representations by end-to-end evolving the logic relations with an edge-reasoning mechanism and updating the graph features. This pipeline is applied to a general encoder, whose fundamental features are joined with the high-level logic features for answer prediction. Experiments on three textual logical reasoning datasets demonstrate the reasonability of the logical structures built in DAGNs and the effectiveness of the learned logic features. Moreover, zero-shot transfer results show the features' generality to unseen logical texts

REM-Net: Recursive Erasure Memory Network for Commonsense Evidence Refinement

When answering a question, people often draw upon their rich world knowledge in addition to the particular context. While recent works retrieve supporting facts/evidence from commonsense knowledge bases to supply additional information to each question, there is still ample opportunity to advance it on the quality of the evidence. It is crucial since the quality of the evidence is the key to answering commonsense questions, and even determines the upper bound on the QA systems performance. In this paper, we propose a recursive erasure memory network (REM-Net) to cope with the quality improvement of evidence. To address this, REM-Net is equipped with a module to refine the evidence by recursively erasing the low-quality evidence that does not explain the question answering. Besides, instead of retrieving evidence from existing knowledge bases, REM-Net leverages a pre-trained generative model to generate candidate evidence customized for the question. We conduct experiments on two commonsense question answering datasets, WIQA and CosmosQA. The results demonstrate the performance of REM-Net and show that the refined evidence is explainable.Comment: Accepted by AAAI 202

Diarrhoea scores and weight changes in response to artificial milk supplementation or use of solulyte-neomycin solution in preweaning piglets

The objective of this study was to determine the effects of supplemental milk replacer and solulyte-neomix solution in preweaning piglets. A total of 199 five-day-old piglets from 22litters were available for this three-week study. 12 litters (110 piglets) were allocated into the milk replacer supplemented group (MILK), five litters (47 piglets) were allocated into the ELEC group which was given an antibiotic-fortified electrolyte solution for pigs, and five litters (45 piglets) remained as untreated control (CTRL). However, after matching for litter size and total litter weights among treatment groups, only 44 piglets (5litters) in the MILK group, 47 piglets (5 litters) in the ELEC group and 45 piglets from 5 litters in the CTRL group were considered in this report. All sows were fed the same diet (18 % protein, 3,952 kcal of ME/kg). Body weights of piglets were measured at days 5 and 25 of age. Fresh liquid commercial milk replacer and solulyte-neomix solution were prepared daily. The fluids were offered thrice daily at 100mL per litter for 5-day-old piglets. Supplementation was increased to 5 times daily at 200mL per litter when piglets were 9 days or older, till the end of the trial. Average litter weight gain was higher in the ELEC piglets given solulyte-neomix solution and creep feed (P<0.05). Milk replacer supplemented group (MILK) generally had lower average litter weight gains at 3.72 kg. However, the diarrhea scores were affected by the types of supplementation fluids given. The overall diarrhoea scores were higher in the MILK and CTRL piglets compared to the ELEC piglets. In conclusion, milk replacer supplementation offered no obvious benefit in terms of weight gain, final weight, and overall diarrhoea scores in piglets compared to solulyte-neomix supplemented piglets

Gender-Differential Associations between Attention Deficit and Hyperactivity Symptoms and Youth Health Risk Behaviors

Attention deficit and hyperactivity disorder (ADHD) is one of the common developmental disorders that generally receives clinical attention at learning ages, and some symptoms may persist in young adulthood.1 Past research has demonstrated a consistent association between ADHD and youth health risk behaviors (e.g., cigarette smoking), which often develop during adolescence and contribute to early morbidity and mortality among young adults.2 However, ADHD symptoms are not routinely screened in adolescents and emerging adults during their visits to healthcare providers.3 The six-item Adult Self-Report Scale (ASRS-6) for ADHD has been validated in the young population for screening purposes.4 This short form is time-saving and also provides a comparable predictivity of ADHD diagnosis as that of the original long version.5 Although accumulating evidence has demonstrated the association between ADHD symptoms and youth health risk behaviors, this issue has scarcely been explored in the Taiwanese youth population.6 Therefore, this study was conducted to validate the psychometric property of the Chinese version of ASRS-6 and examine the gender-stratified association between ADHD symptoms and youth health risk behaviors