261 research outputs found
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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
SoK: Security of Cross-chain Bridges: Attack Surfaces, Defenses, and Open Problems
Cross-chain bridges are used to facilitate token and data exchanges across
blockchains. Although bridges are becoming increasingly popular, they are still
in their infancy and have been attacked multiple times recently, causing
significant financial loss. Although there are numerous reports online
explaining each of the incidents on cross-chain bridges, they are scattered
over the Internet, and there is no work that analyzes the security landscape of
cross-chain bridges in a holistic manner. To fill the gap, in this paper, we
performed a systematic study of cross-chain bridge security issues. First, we
summarize the characteristics of existing cross-chain bridges, including their
usages, verification mechanisms, communication models, and three
categorizations. Based on these characteristics, we identify 12 potential
attack vectors that attackers may exploit. Next, we introduce a taxonomy that
categorizes cross-chain attacks in the past two years into 10 distinct types,
and then provide explanations for each vulnerability type, accompanied by
Solidity code examples. We also discuss existing and potential defenses, as
well as open questions and future research directions on cross-chain bridges.
We believe that this systematization can shed light on designing and
implementing cross-chain bridges with higher security and, more importantly,
facilitating future research on building a better cross-chain bridge ecosystem
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 , where 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 -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
SME: 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 (SME). 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
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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
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