The interplay between selective attention and working memory: A behavioural, neural and computational perspective

Selective attention (SA), the process by which information is prioritized for processing according to its relevance to current goals, and working memory (WM), the temporary storage and/or manipulation of information in mind, are considered to be important building blocks in human cognition. Both are essential for coordinating thought and action, and both are foundational for the emergence of other more complex executive functions, like planning and problem solving. The complicated interplay between SA and WM has been investigated across a growing number of experimental studies, with attentional processes influencing various stages of WM, and vice versa. Behavioural evidence suggests that SA can bias processing of information as we anticipate, encode and maintain contents in memory, whilst WM can serve to maintain a template as we search. Neuroimaging studies have observed a highly similar frontoparietal network subserving both processes, indicating anatomical and functional overlap in their corresponding neural mechanisms. Nevertheless, despite substantial evidence for cognitive and neural overlap, almost everything we know about the relationship between SA and WM is derived using group-average performance. In reality, some individuals may rely on shared sub-processes to perform tasks more so than others. In this thesis we extended previous work by understanding this individual variability. The first experimental chapter describes the development of two behavioural paradigms tapping SA and WM. These paradigms are better suited to address this question, relative to previous experimental approaches, because they are matched on task-specific features while being independently scalable in terms of difficulty. The second experimental chapter used functional magnetic resonance imaging (fMRI) in combination with these tasks to identify the neural correlates of individual differences in the strength of SA-WM coupling across participants. The third experimental chapter builds upon the neuroimaging study and addresses whether computational models trained to perform the same set of tasks share any mechanistic properties observed in the human brain, providing a useful framework in which predictions about the relationship between cognitive processes can be readily tested. Lastly, in the final experiment we used cognitive training to test whether altering SA would lead to changes in the related WM system, and whether these gains are modulated by baseline individual differences in the strength of their coupling. Together, along with an opening General Introduction and concluding Discussion, these chapters explore heterogeneity in the relationship between SA and WM from multiple perspectives, integrating advances in human cognition, neuroimaging and computational modelling.Cambridge Trust Chinese Scholarship Counci

Triangular algebras with nonlinear higher Lie n-derivation by local actions

This paper was devoted to the study of the so-called nonlinear higher Lie n-derivation of triangular algebras $\mathcal{T}$, where $n$ is a nonnegative integer greater than two. Under some mild conditions, we proved that every nonlinear higher Lie n-derivation by local actions on the triangular algebras is of a standard form. As an application, we gave a characterization of higher Lie $n$-derivation by local actions on upper triangular matrix algebras, block upper triangular matrix algebras and nest algebras, respectively

The cingulum as a marker of individual differences in neurocognitive development.

The canonical approach to exploring brain-behaviour relationships is to group individuals according to a phenotype of interest, and then explore the neural correlates of this grouping. A limitation of this approach is that multiple aetiological pathways could result in a similar phenotype, so the role of any one brain mechanism may be substantially underestimated. Building on advances in network analysis, we used a data-driven community-clustering algorithm to identify robust subgroups based on white-matter microstructure in childhood and adolescence (total N = 313, mean age: 11.24 years). The algorithm indicated the presence of two equal-size groups that show a critical difference in fractional anisotropy (FA) of the left and right cingulum. Applying the brain-based grouping in independent samples, we find that these different 'brain types' had profoundly different cognitive abilities with higher performance in the higher FA group. Further, a connectomics analysis indicated reduced structural connectivity in the low FA subgroup that was strongly related to reduced functional activation of the default mode network. These results provide a proof-of-concept that bottom-up brain-based groupings can be identified that relate to cognitive performance. This provides a first demonstration of a complimentary approach for investigating individual differences in brain structure and function, particularly for neurodevelopmental disorders where researchers are often faced with phenotypes that are difficult to define at the cognitive or behavioural level.The Centre for Attention Learning and Memory (CALM) research clinic is based at and supported by funding from the MRC Cognition and Brain Sciences Unit, University of Cambridge

SAM Meets Robotic Surgery: An Empirical Study on Generalization, Robustness and Adaptation

The Segment Anything Model (SAM) serves as a fundamental model for semantic segmentation and demonstrates remarkable generalization capabilities across a wide range of downstream scenarios. In this empirical study, we examine SAM's robustness and zero-shot generalizability in the field of robotic surgery. We comprehensively explore different scenarios, including prompted and unprompted situations, bounding box and points-based prompt approaches, as well as the ability to generalize under corruptions and perturbations at five severity levels. Additionally, we compare the performance of SAM with state-of-the-art supervised models. We conduct all the experiments with two well-known robotic instrument segmentation datasets from MICCAI EndoVis 2017 and 2018 challenges. Our extensive evaluation results reveal that although SAM shows remarkable zero-shot generalization ability with bounding box prompts, it struggles to segment the whole instrument with point-based prompts and unprompted settings. Furthermore, our qualitative figures demonstrate that the model either failed to predict certain parts of the instrument mask (e.g., jaws, wrist) or predicted parts of the instrument as wrong classes in the scenario of overlapping instruments within the same bounding box or with the point-based prompt. In fact, SAM struggles to identify instruments in complex surgical scenarios characterized by the presence of blood, reflection, blur, and shade. Additionally, SAM is insufficiently robust to maintain high performance when subjected to various forms of data corruption. We also attempt to fine-tune SAM using Low-rank Adaptation (LoRA) and propose SurgicalSAM, which shows the capability in class-wise mask prediction without prompt. Therefore, we can argue that, without further domain-specific fine-tuning, SAM is not ready for downstream surgical tasks.Comment: Accepted as Oral Presentation at MedAGI Workshop - MICCAI 2023 1st International Workshop on Foundation Models for General Medical AI. arXiv admin note: substantial text overlap with arXiv:2304.1467

S2^2ME: Spatial-Spectral Mutual Teaching and Ensemble Learning for Scribble-supervised Polyp Segmentation

Fully-supervised polyp segmentation has accomplished significant triumphs over the years in advancing the early diagnosis of colorectal cancer. However, label-efficient solutions from weak supervision like scribbles are rarely explored yet primarily meaningful and demanding in medical practice due to the expensiveness and scarcity of densely-annotated polyp data. Besides, various deployment issues, including data shifts and corruption, put forward further requests for model generalization and robustness. To address these concerns, we design a framework of Spatial-Spectral Dual-branch Mutual Teaching and Entropy-guided Pseudo Label Ensemble Learning (S$^2$ME). Concretely, for the first time in weakly-supervised medical image segmentation, we promote the dual-branch co-teaching framework by leveraging the intrinsic complementarity of features extracted from the spatial and spectral domains and encouraging cross-space consistency through collaborative optimization. Furthermore, to produce reliable mixed pseudo labels, which enhance the effectiveness of ensemble learning, we introduce a novel adaptive pixel-wise fusion technique based on the entropy guidance from the spatial and spectral branches. Our strategy efficiently mitigates the deleterious effects of uncertainty and noise present in pseudo labels and surpasses previous alternatives in terms of efficacy. Ultimately, we formulate a holistic optimization objective to learn from the hybrid supervision of scribbles and pseudo labels. Extensive experiments and evaluation on four public datasets demonstrate the superiority of our method regarding in-distribution accuracy, out-of-distribution generalization, and robustness, highlighting its promising clinical significance. Our code is available at https://github.com/lofrienger/S2ME.Comment: MICCAI 2023 Early Acceptanc

Central Asia: The Vanguard in Jointly Building the «Belt & Road» Community of Shared Future for Mankind

The Silk Road originated in China, while Central Asia served as the crossroads of the Eurasian region. In 140 BC, during the Han Dynasty, Zhang Qian embarked on a mission to the Western Regions, present-day Central Asia. He paved the way from the East to the West, completing a challenging journey. President Xi proposed constructing the Silk Road Economic Belt (SREB) in Kazakhstan, making Central Asia the starting point and the first western station of the Belt and Road Initiative (BRI). Central Asia has always been at the forefront of building the BRI, setting an example for constructing a community with a shared future for humanity

Decreased buffering capacity and increased recovery time for legacy phosphorus in a typical watershed in eastern China between 1960 and 2010

Legacy phosphorus (P) accumulated in watersheds from excessive historical P inputs is recognized as an important component of water pollution control and sustainable P management in watersheds worldwide. However, little is known about how watershed P buffering capacity responds to legacy P pressures over time and how long it takes for riverine P concentrations to recover to a target level, especially in developing countries. This study examined long-term (1960–2010) accumulated legacy P stock, P buffering capacity and riverine TP flux dynamics to predict riverine P-reduction recovery times in the Yongan watershed of eastern China. Due to a growing legacy P stock coupled with changes in land use and climate, estimated short- and long-term buffering metrics (i.e., watershed ability to retain current year and historically accumulated surplus P, respectively) decreased by 65% and 36%, respectively, resulting in a 15-fold increase of riverine P flux between 1980 and 2010. An empirical model incorporating accumulated legacy P stock and annual precipitation was developed (R2 = 0.99) and used to estimate a critical legacy P stock of 22.2 ton P km−2 (95% CI 19.4–25.3 ton P km−2) that would prevent exceedance of a target riverine TP concentration of 0.05 mg P L−1. Using an exponential decay model, the recovery time for depleting the estimated legacy P stock in 2010 (29.3 ton P km−2) to the critical level (22.2 ton P km−2) via riverine flux was 456 years (95% CI 353–560 years), 159 years (95% CI 57–262 years) and 318 years (95% CI 238–400 years) under scenarios of a 4% reduction in annual P inputs, total cessation of P inputs, and 4% reduction of annual P inputs with a 10% increase in average annual precipitation, respectively. Given the lower P buffering capacity and lengthening recovery time, strategies to reduce P inputs and utilize soil legacy P for crop production are necessary to effectively control riverine P pollution and conserve global rock P resources. A long-term perspective that incorporates both contemporary and historical information is required for developing sustainable P management strategies to optimize both agronomic and environmental benefits at the watershed scale

Modeling Spatiotemporal Periodicity and Collaborative Signal for Local-Life Service Recommendation

Online local-life service platforms provide services like nearby daily essentials and food delivery for hundreds of millions of users. Different from other types of recommender systems, local-life service recommendation has the following characteristics: (1) spatiotemporal periodicity, which means a user's preferences for items vary from different locations at different times. (2) spatiotemporal collaborative signal, which indicates similar users have similar preferences at specific locations and times. However, most existing methods either focus on merely the spatiotemporal contexts in sequences, or model the user-item interactions without spatiotemporal contexts in graphs. To address this issue, we design a new method named SPCS in this paper. Specifically, we propose a novel spatiotemporal graph transformer (SGT) layer, which explicitly encodes relative spatiotemporal contexts, and aggregates the information from multi-hop neighbors to unify spatiotemporal periodicity and collaborative signal. With extensive experiments on both public and industrial datasets, this paper validates the state-of-the-art performance of SPCS.Comment: KDAH CIKM'23 Worksho

Functional network dynamics in a neurodevelopmental disorder of known genetic origin.

Dynamic connectivity in functional brain networks is a fundamental aspect of cognitive development, but we have little understanding of the mechanisms driving variability in these networks. Genes are likely to influence the emergence of fast network connectivity via their regulation of neuronal processes, but novel methods to capture these rapid dynamics have rarely been used in genetic populations. The current study redressed this by investigating brain network dynamics in a neurodevelopmental disorder of known genetic origin, by comparing individuals with a ZDHHC9-associated intellectual disability to individuals with no known impairment. We characterised transient network dynamics using a Hidden Markov Model (HMM) on magnetoencephalography (MEG) data, at rest and during auditory oddball stimulation. The HMM is a data-driven method that captures rapid patterns of coordinated brain activity recurring over time. Resting-state network dynamics distinguished the groups, with ZDHHC9 participants showing longer state activation and, crucially, ZDHHC9 gene expression levels predicted the group differences in dynamic connectivity across networks. In contrast, network dynamics during auditory oddball stimulation did not show this association. We demonstrate a link between regional gene expression and brain network dynamics, and present the new application of a powerful method for understanding the neural mechanisms linking genetic variation to cognitive difficulties