CIDR: A Cooperative Integrated Dynamic Refining Method for Minimal Feature Removal Problem

The minimal feature removal problem in the post-hoc explanation area aims to identify the minimal feature set (MFS). Prior studies using the greedy algorithm to calculate the minimal feature set lack the exploration of feature interactions under a monotonic assumption which cannot be satisfied in general scenarios. In order to address the above limitations, we propose a Cooperative Integrated Dynamic Refining method (CIDR) to efficiently discover minimal feature sets. Specifically, we design Cooperative Integrated Gradients (CIG) to detect interactions between features. By incorporating CIG and characteristics of the minimal feature set, we transform the minimal feature removal problem into a knapsack problem. Additionally, we devise an auxiliary Minimal Feature Refinement algorithm to determine the minimal feature set from numerous candidate sets. To the best of our knowledge, our work is the first to address the minimal feature removal problem in the field of natural language processing. Extensive experiments demonstrate that CIDR is capable of tracing representative minimal feature sets with improved interpretability across various models and datasets.Comment: Accepted by AAAI202

Learning Knowledge-Enhanced Contextual Language Representations for Domain Natural Language Understanding

Knowledge-Enhanced Pre-trained Language Models (KEPLMs) improve the performance of various downstream NLP tasks by injecting knowledge facts from large-scale Knowledge Graphs (KGs). However, existing methods for pre-training KEPLMs with relational triples are difficult to be adapted to close domains due to the lack of sufficient domain graph semantics. In this paper, we propose a Knowledge-enhanced lANGuAge Representation learning framework for various clOsed dOmains (KANGAROO) via capturing the implicit graph structure among the entities. Specifically, since the entity coverage rates of closed-domain KGs can be relatively low and may exhibit the global sparsity phenomenon for knowledge injection, we consider not only the shallow relational representations of triples but also the hyperbolic embeddings of deep hierarchical entity-class structures for effective knowledge fusion.Moreover, as two closed-domain entities under the same entity-class often have locally dense neighbor subgraphs counted by max point biconnected component, we further propose a data augmentation strategy based on contrastive learning over subgraphs to construct hard negative samples of higher quality. It makes the underlying KELPMs better distinguish the semantics of these neighboring entities to further complement the global semantic sparsity. In the experiments, we evaluate KANGAROO over various knowledge-aware and general NLP tasks in both full and few-shot learning settings, outperforming various KEPLM training paradigms performance in closed-domains significantly.Comment: emnlp 202

Social intelligence mediates the protective role of resting-state brain activity in the social cognition network against social anxiety

Abstract Background Social intelligence refers to an important psychosocial skill set encompassing an array of abilities, including effective self-expression, understanding of social contexts, and acting wisely in social interactions. While there is ample evidence of its importance in various mental health outcomes, particularly social anxiety, little is known on the brain correlates underlying social intelligence and how it can mitigate social anxiety. Objective This research aims to investigate the functional neural markers of social intelligence and their relations to social anxiety. Methods Data of resting-state functional magnetic resonance imaging and behavioral measures were collected from 231 normal students aged 16 to 20 years (48% male). Whole-brain voxel-wise correlation analysis was conducted to detect the functional brain clusters related to social intelligence. Correlation and mediation analyses explored the potential role of social intelligence in the linkage of resting-state brain activities to social anxiety. Results Social intelligence was correlated with neural activities (assessed as the fractional amplitude of low-frequency fluctuations, fALFF) among two key brain clusters in the social cognition networks: negatively correlated in left superior frontal gyrus (SFG) and positively correlated in right middle temporal gyrus (MTG). Further, the left SFG fALFF was positively correlated with social anxiety; brain–personality-symptom analysis revealed that this relationship was mediated by social intelligence. Conclusion These results indicate that resting-state activities in the social cognition networks might influence a person's social anxiety via social intelligence: lower left SFG activity → higher social intelligence → lower social anxiety. These may have significance for developing neurobehavioral intervention to mitigate social anxiety. </jats:sec

Dissociable Early Attentional Control Mechanisms Underlying Cognitive and Affective Conflicts

It has been well documented that cognitive conflict is sensitive to the relative proportion of congruent and incongruent trials. However, few studies have examined whether affective conflict processing is modulated as a function of proportion congruency (PC). To address this question we recorded eventrelated potentials (ERP) while subjects performed both cognitive and affective face-word Stroop tasks. By varying the proportion of congruent and incongruent trials in each block, we examined the extent to which PC impacts both cognitive and affective conflict control at different temporal stages. Results showed that in the cognitive task an anteriorly localized early N2 component occurred predominantly in the low proportion congruency context, whereas in the affective task it was found to occur in the high proportion congruency one. The N2 effects across the two tasks were localized to the dorsolateral prefrontal cortex, where responses were increased in the cognitive task but decreased in the affective one. Furthermore, high proportions of congruent items produced both larger amplitude of a posteriorly localized sustained potential component and a larger behavioral Stroop effect in cognitive and affective tasks. Our findings suggest that cognitive and affective conflicts engage early dissociable attentional control mechanisms and a later common conflict response system

AI-based dimensional neuroimaging system for characterizing heterogeneity in brain structure and function in major depressive disorder:COORDINATE-MDD consortium design and rationale

BACKGROUND: Efforts to develop neuroimaging-based biomarkers in major depressive disorder (MDD), at the individual level, have been limited to date. As diagnostic criteria are currently symptom-based, MDD is conceptualized as a disorder rather than a disease with a known etiology; further, neural measures are often confounded by medication status and heterogeneous symptom states. METHODS: We describe a consortium to quantify neuroanatomical and neurofunctional heterogeneity via the dimensions of novel multivariate coordinate system (COORDINATE-MDD). Utilizing imaging harmonization and machine learning methods in a large cohort of medication-free, deeply phenotyped MDD participants, patterns of brain alteration are defined in replicable and neurobiologically-based dimensions and offer the potential to predict treatment response at the individual level. International datasets are being shared from multi-ethnic community populations, first episode and recurrent MDD, which are medication-free, in a current depressive episode with prospective longitudinal treatment outcomes and in remission. Neuroimaging data consist of de-identified, individual, structural MRI and resting-state functional MRI with additional positron emission tomography (PET) data at specific sites. State-of-the-art analytic methods include automated image processing for extraction of anatomical and functional imaging variables, statistical harmonization of imaging variables to account for site and scanner variations, and semi-supervised machine learning methods that identify dominant patterns associated with MDD from neural structure and function in healthy participants. RESULTS: We are applying an iterative process by defining the neural dimensions that characterise deeply phenotyped samples and then testing the dimensions in novel samples to assess specificity and reliability. Crucially, we aim to use machine learning methods to identify novel predictors of treatment response based on prospective longitudinal treatment outcome data, and we can externally validate the dimensions in fully independent sites. CONCLUSION: We describe the consortium, imaging protocols and analytics using preliminary results. Our findings thus far demonstrate how datasets across many sites can be harmonized and constructively pooled to enable execution of this large-scale project

Underwater Acoustic Time Delay Estimation Based on Envelope Differences of Correlation Functions

This paper proposes underwater acoustic time delay estimation based on the envelope differences of correlation functions (EDCF), which mitigates the delay estimation errors introduced by the amplitude fluctuations of the correlation function envelopes in the traditional correlation methods (CM). The performance of the proposed delay estimation method under different time values was analyzed, and the optimal difference time values are given. To overcome the influences of digital signal sampling intervals on time delay estimation, a digital time delay estimation approach with low complexity and high accuracy is proposed. The performance of the proposed time delay estimation was analyzed in underwater multipath channels. Finally, the accuracy of the delay estimation using this proposed method was demonstrated by experiments

Some characteristics and taxonomic classification of ferrallitic soils in Eastern part of tropical and subtropical zone of China

In the present paper some characteristics of seventy ferrallitic soil profiles in three selected soil transects located in the eastern part of tropical and subtropical zones of China are discussed, and the taxonomic classification of the soils is made according to the differences of diagnostic horizons and/or diagnostic characteristics, which are connected with soil-forming processes. All these soils studied are classified into four orders : ferralisols, ferrisols, luvisols and cambisols, and their subdivisions into suborders, groups and subgroups are also proposed. (Résumé d'auteur

Some characteristics and taxonomic classification of ferrallitic soils in Eastern part of tropical and subtropical zone of China

In the present paper some characteristics of seventy ferrallitic soil profiles in three selected soil transects located in the eastern part of tropical and subtropical zones of China are discussed, and the taxonomic classification of the soils is made according to the differences of diagnostic horizons and/or diagnostic characteristics, which are connected with soil-forming processes. All these soils studied are classified into four orders : ferralisols, ferrisols, luvisols and cambisols, and their subdivisions into suborders, groups and subgroups are also proposed. (Résumé d'auteur

Performance analysis and evaluation for active antenna arrays under three-dimensional wireless channel model

In this paper, we establish a full 3-D channel model to support the performance analysis and evaluation of active antenna array (AAA)-based wireless communication systems. We analyze and compare the impact of three different downtilt methods employed in AAA antennas, electrical downtilt (ET), mechanical downtilt (MT), hybrid downtilt (the combination of ET and MT), on the antenna patterns, which would notably impact the performance of mobile wireless communication systems. We compare the performances of the wireless communication system throughput based on the 2-D and 3-D wireless channel models using passive antenna arrays. We also investigate the system performance in terms of the capacity and coverage with different AAAs under the 3-D channel. After the performance analysis and evaluation, we have observed that the downtilt optimization may introduce significant gains in coverage and capacity for individual antennas with smaller beamwidth of the vertical patterns, but may not lead to notable gains for individual antennas with relative larger beamwidth of the vertical patterns.Basic Research Foundation of Chinese Academy of Fishery Sciences Research and Validation of Fishing Vessel Network Architecture 2017HY-ZD0801 Joint Laboratory for Deep Blue Fishering Engineering Qingdao National Laboratory for Marine Science and Technology, Chin