Salivary testosterone and cortisol response in acute stress modulated by seven sessions of mindfulness meditation in young males

Stress is an established risk factor for negative health outcomes. Salivary cortisol and testosterone concentrations increase in response to acute psychosocial stress. It’s crucial to reduce stress for health and well-being through evidence-based interventions. Body-mind interventions such as meditation and Tai Chi have shown reduced cortisol levels but mixed results in testosterone concentration after stress. To address this research gap, we conducted a pilot randomized controlled trial to examine the modulating effects of a short-term (seven 20-minute sessions) mindfulness meditation on testosterone and cortisol in response to acute stress. Using one form of mindfulness meditation – Integrative Body-Mind Training (IBMT) and an active control–relaxation training (RT), we assessed salivary cortisol and testosterone concentrations at three stages of stress intervention – rest, stress, and an additional 20-min IBMT or RT practice. We found increased cortisol and testosterone concentrations after acute stress in both groups, but testosterone rise was not associated with cortisol rise. Moreover, an additional practice immediately after stress produced higher testosterone concentrations in the IBMT group than the RT group, whereas cortisol concentration increased in the RT group than in the IBMT group at the same time point. These findings indicate that brief mindfulness intervention modulates a dual-hormone profile of testosterone and cortisol in response to acute stress presumably via the co-regulation of hypothalamus-pituitary-adrenal and hypothalamus-pituitary-testicular axes

Biologically inspired structure learning with reverse knowledge distillation for spiking neural networks

Spiking neural networks (SNNs) have superb characteristics in sensory information recognition tasks due to their biological plausibility. However, the performance of some current spiking-based models is limited by their structures which means either fully connected or too-deep structures bring too much redundancy. This redundancy from both connection and neurons is one of the key factors hindering the practical application of SNNs. Although Some pruning methods were proposed to tackle this problem, they normally ignored the fact the neural topology in the human brain could be adjusted dynamically. Inspired by this, this paper proposed an evolutionary-based structure construction method for constructing more reasonable SNNs. By integrating the knowledge distillation and connection pruning method, the synaptic connections in SNNs can be optimized dynamically to reach an optimal state. As a result, the structure of SNNs could not only absorb knowledge from the teacher model but also search for deep but sparse network topology. Experimental results on CIFAR100 and DVS-Gesture show that the proposed structure learning method can get pretty well performance while reducing the connection redundancy. The proposed method explores a novel dynamical way for structure learning from scratch in SNNs which could build a bridge to close the gap between deep learning and bio-inspired neural dynamics

Case report: Thoughts on two cases of total anomalous pulmonary venous connection complicated with pulmonary artery hypertension

The two primary pathological alterations of total anomalous pulmonary venous connection (TAPVC), a rare cyanotic congenital heart disease (CHD), are right heart failure and pulmonary artery hypertension (PAH). The timing and prognosis of surgery depend on the level of pulmonary hypertension. Surgery will not be an option after Eisenmenger syndrome appears. In light of this, it is crucial to assess patients’ PAH. In order to aid in the following treatment of related types of diseases, this article studied and compared the echocardiographic features and disease development of one adult and one child TAPVC patients complicated with PAH

Exploring Memorization in Fine-tuned Language Models

LLMs have shown great capabilities in various tasks but also exhibited memorization of training data, thus raising tremendous privacy and copyright concerns. While prior work has studied memorization during pre-training, the exploration of memorization during fine-tuning is rather limited. Compared with pre-training, fine-tuning typically involves sensitive data and diverse objectives, thus may bring unique memorization behaviors and distinct privacy risks. In this work, we conduct the first comprehensive analysis to explore LMs' memorization during fine-tuning across tasks. Our studies with open-sourced and our own fine-tuned LMs across various tasks indicate that fine-tuned memorization presents a strong disparity among tasks. We provide an understanding of this task disparity via sparse coding theory and unveil a strong correlation between memorization and attention score distribution. By investigating its memorization behavior, multi-task fine-tuning paves a potential strategy to mitigate fine-tuned memorization

Visual Robotic Manipulation with Depth-Aware Pretraining

Recent work on visual representation learning has shown to be efficient for robotic manipulation tasks. However, most existing works pretrained the visual backbone solely on 2D images or egocentric videos, ignoring the fact that robots learn to act in 3D space, which is hard to learn from 2D observation. In this paper, we examine the effectiveness of pretraining for vision backbone with public-available large-scale 3D data to improve manipulation policy learning. Our method, namely Depth-aware Pretraining for Robotics (DPR), enables an RGB-only backbone to learn 3D scene representations from self-supervised contrastive learning, where depth information serves as auxiliary knowledge. No 3D information is necessary during manipulation policy learning and inference, making our model enjoy both efficiency and effectiveness in 3D space manipulation. Furthermore, we introduce a new way to inject robots' proprioception into the policy networks that makes the manipulation model robust and generalizable. We demonstrate in experiments that our proposed framework improves performance on unseen objects and visual environments for various robotics tasks on both simulated and real robots.Comment: submitted to ICRA202