Cheng Wang

U-Net has become a cornerstone in various visual applications such as image segmentation and diffusion probability models. While numerous innovative designs and improvements have been introduced by incorporating transformers or MLPs, the networks are still limited to linearly modeling patterns as well as the deficient interpretability. To address these challenges, our intuition is inspired by the impressive results of the Kolmogorov-Arnold Networks (KANs) in terms of accuracy and interpretability, which reshape the neural network learning via the stack of non-linear learnable activation functions derived from the Kolmogorov-Anold representation theorem. Specifically, in this paper, we explore the untapped potential of KANs in improving backbones for vision tasks. We investigate, modify and re-design the established U-Net pipeline by integrating the dedicated KAN layers on the tokenized intermediate representation, termed U-KAN. Rigorous medical image segmentation benchmarks verify the superiority of UKAN by higher accuracy even with less computation cost. We further delved into the potential of U-KAN as an alternative U-Net noise predictor in diffusion models, demonstrating its applicability in generating task-oriented model architectures.

The current study utilized resting-state functional magnetic resonance imaging (fMRI) to examine how two important non-cognitive skills, grit and growth mindset, are associated with cortico-striatal networks important for learning. Whole-brain seed-to-voxel connectivity was examined for dorsal and ventral striatal seeds. While both grit and growth mindset were associated with functional connectivity between ventral striatal and bilateral prefrontal networks thought to be important for cognitive-behavioral control. There were also clear dissociations between the neural correlates of the two constructs. Grit, the long-term perseverance towards a goal or set of goals, was associated with ventral striatal networks including connectivity to regions such as the medial prefrontal and rostral anterior cingulate cortices implicated in perseverance, delay and receipt of reward. Growth mindset, the belief that effort can improve talents, notably intelligence, was associated with both ventral and dorsal striatal connectivity with regions thought to be important for error-monitoring, such as dorsal anterior cingulate cortex. Our findings may help construct neurocognitive models of these non-cognitive skills and have critical implications for character education. Such education is a key component of social and emotional learning, ensuring that children can rise to challenges in the classroom and in life.

U-Net has become a cornerstone in various visual applications such as image segmentation and diffusion probability models. While numerous innovative designs and improvements have been introduced by incorporating transformers or MLPs, the networks are still limited to linearly modeling patterns as well as the deficient interpretability. To address these challenges, our intuition is inspired by the impressive results of the Kolmogorov-Arnold Networks (KANs) in terms of accuracy and interpretability, which reshape the neural network learning via the stack of non-linear learnable activation functions derived from the Kolmogorov-Anold representation theorem. Specifically, in this paper, we explore the untapped potential of KANs in improving backbones for vision tasks. We investigate, modify and re-design the established U-Net pipeline by integrating the dedicated KAN layers on the tokenized intermediate representation, termed U-KAN. Rigorous medical image segmentation benchmarks verify the superiority of U-KAN by higher accuracy even with less computation cost. We further delved into the potential of U-KAN as an alternative U-Net noise predictor in diffusion models, demonstrating its applicability in generating task-oriented model architectures. These endeavours unveil valuable insights and sheds light on the prospect that with U-KAN, you can make strong backbone for medical image segmentation and generation. Project page:\url{https://yes-u-kan.github.io/}.

It has been widely documented that word frequency (WF) modulates language processing in various input and output modalities. WF effect has also been reported in the domain of written production; however, how WF affects written production is a controversial issue. The present study attempts to investigate the time course of and neural oscillation underlying the WF effect in handwritten production. Participants were asked to handwrite pictures names of high versus low WF, while concurrently recording EEG. EEG trials were extracted time-locked to picture onsets and then submitted to event-related potential analysis and time-frequency analysis. WF affected ERPs in the time windows of around 98-160 and 282-360 ms after picture onsets. More importantly, WF modulated the evoked and induced theta-band (4-8 Hz) neural oscillations in the time window of around 36-240 and 244-472 ms, respectively. Considering the time course of language production and the role of theta-band oscillation in long-term memory retrieval, we suggest that the two stages of the WF effect, respectively, reflect conceptual preparation and retrieval of orthographic word-forms in written production.