AntGPT: Can Large Language Models Help Long-term Action Anticipation from Videos?

Can we better anticipate an actor's future actions (e.g. mix eggs) by knowing what commonly happens after his/her current action (e.g. crack eggs)? What if we also know the longer-term goal of the actor (e.g. making egg fried rice)? The long-term action anticipation (LTA) task aims to predict an actor's future behavior from video observations in the form of verb and noun sequences, and it is crucial for human-machine interaction. We propose to formulate the LTA task from two perspectives: a bottom-up approach that predicts the next actions autoregressively by modeling temporal dynamics; and a top-down approach that infers the goal of the actor and plans the needed procedure to accomplish the goal. We hypothesize that large language models (LLMs), which have been pretrained on procedure text data (e.g. recipes, how-tos), have the potential to help LTA from both perspectives. It can help provide the prior knowledge on the possible next actions, and infer the goal given the observed part of a procedure, respectively. To leverage the LLMs, we propose a two-stage framework, AntGPT. It first recognizes the actions already performed in the observed videos and then asks an LLM to predict the future actions via conditioned generation, or to infer the goal and plan the whole procedure by chain-of-thought prompting. Empirical results on the Ego4D LTA v1 and v2 benchmarks, EPIC-Kitchens-55, as well as EGTEA GAZE+ demonstrate the effectiveness of our proposed approach. AntGPT achieves state-of-the-art performance on all above benchmarks, and can successfully infer the goal and thus perform goal-conditioned "counterfactual" prediction via qualitative analysis. Code and model will be released at https://brown-palm.github.io/AntGP

Mutual Information-Based Brain Network Analysis in Post-stroke Patients With Different Levels of Depression

Post-stroke depression (PSD) is the most common stroke-related emotional disorder, and it severely affects the recovery process. However, more than half cases are not correctly diagnosed. This study was designed to develop a new method to assess PSD using EEG signal to analyze the specificity of PSD patients' brain network. We have 107 subjects attended in this study (72 stabilized stroke survivors and 35 non-depressed healthy subjects). A Hamilton Depression Rating Scale (HDRS) score was determined for all subjects before EEG data collection. According to HDRS score, the 72 patients were divided into 3 groups: post-stroke non-depression (PSND), post-stroke mild depression (PSMD) and post-stroke depression (PSD). Mutual information (MI)-based graph theory was used to analyze brain network connectivity. Statistical analysis of brain network characteristics was made with a threshold of 10–30% of the strongest MIs. The results showed significant weakened interhemispheric connections and lower clustering coefficient in post-stroke depressed patients compared to those in healthy controls. Stroke patients showed a decreasing trend in the connection between the parietal-occipital and the frontal area as the severity of the depression increased. PSD subjects showed abnormal brain network connectivity and network features based on EEG, suggesting that MI-based brain network may have the potential to assess the severity of depression post stroke

Inactivation of a Novel FGF23 Regulator, FAM20C, Leads to Hypophosphatemic Rickets in Mice

Family with sequence similarity 20,-member C (FAM20C) is highly expressed in the mineralized tissues of mammals. Genetic studies showed that the loss-of-function mutations in FAM20C were associated with human lethal osteosclerotic bone dysplasia (Raine Syndrome), implying an inhibitory role of this molecule in bone formation. However, in vitro gain- and loss-of-function studies suggested that FAM20C promotes the differentiation and mineralization of mouse mesenchymal cells and odontoblasts. Recently, we generated Fam20c conditional knockout (cKO) mice in which Fam20c was globally inactivated (by crossbreeding with Sox2-Cre mice) or inactivated specifically in the mineralized tissues (by crossbreeding with 3.6 kb Col 1a1-Cre mice). Fam20c transgenic mice were also generated and crossbred with Fam20c cKO mice to introduce the transgene in the knockout background. In vitro gain- and loss-of-function were examined by adding recombinant FAM20C to MC3T3-E1 cells and by lentiviral shRNA–mediated knockdown of FAM20C in human and mouse osteogenic cell lines. Surprisingly, both the global and mineralized tissue-specific cKO mice developed hypophosphatemic rickets (but not osteosclerosis), along with a significant downregulation of osteoblast differentiation markers and a dramatic elevation of fibroblast growth factor 23 (FGF23) in the serum and bone. The mice expressing the Fam20c transgene in the wild-type background showed no abnormalities, while the expression of the Fam20c transgene fully rescued the skeletal defects in the cKO mice. Recombinant FAM20C promoted the differentiation and mineralization of MC3T3-E1 cells. Knockdown of FAM20C led to a remarkable downregulation of DMP1, along with a significant upregulation of FGF23 in both human and mouse osteogenic cell lines. These results indicate that FAM20C is a bone formation “promoter” but not an “inhibitor” in mouse osteogenesis. We conclude that FAM20C may regulate osteogenesis through its direct role in facilitating osteoblast differentiation and its systemic regulation of phosphate homeostasis via the mediation of FGF23

The Advantage of Low-Delta Electroencephalogram Phase Feature for Reconstructing the Center-Out Reaching Hand Movements

It is an emerging frontier of research on the use of neural signals for prosthesis control, in order to restore lost function to amputees and patients after spinal cord injury. Compared to the invasive neural signal based brain-machine interface (BMI), a non-invasive alternative, i.e., the electroencephalogram (EEG)-based BMI would be more widely accepted by the patients above. Ideally, a real-time continuous neuroprosthestic control is required for practical applications. However, conventional EEG-based BMIs mainly deal with the discrete brain activity classification. Until recently, the literature has reported several attempts for achieving the real-time continuous control by reconstructing the continuous movement parameters (e.g., speed, position, etc.) from the EEG recordings, and the low-frequency band EEG is consistently reported to encode the continuous motor control information. Previous studies with executed movement tasks have extensively relied on the amplitude representation of such slow oscillations of EEG signals for building models to decode kinematic parameters. Inspired by the recent successes of instantaneous phase of low-frequency invasive brain signals in the motor control and sensory processing domains, this study examines the extension of such a slow-oscillation phase representation to the reconstructing two-dimensional hand movements, with the non-invasive EEG signals for the first time. The data for analysis are collected on five healthy subjects performing 2D hand center-out reaching along four directions in two sessions. On representative channels over the cortices encoding the execution information of reaching movements, we show that the low-delta EEG phase representation is characterized by higher signal-to-noise ratio and stronger modulation by the movement tasks, compared to the low-delta EEG amplitude representation. Furthermore, we have tested the low-delta EEG phase representation with two commonly used linear decoding models. The results demonstrate that the low-delta EEG phase based decoders lead to superior performance for 2D executed movement reconstruction to its amplitude based counterparts, as well as the other-frequency band amplitude and power based features. Thus, our study contributes to improve the movement reconstruction from EEG by introducing a new feature set based on the low-delta EEG phase patterns, and demonstrates its potential for continuous fine motion control of neuroprostheses

Prognosis of Lithium-Ion Batteries’ Remaining Useful Life Based on a Sequence-to-Sequence Model with Variational Mode Decomposition

The time-varying, dynamic, nonlinear, and other characteristics of lithium-ion batteries, as well as the capacity regeneration phenomenon, leads to the low accuracy of the traditional deep learning models in predicting the remaining useful life of lithium-ion batteries. This paper established a sequence-to-sequence model for remaining useful life prediction by combining the variational modal decomposition with bi-directional long short-term memory and Bayesian hyperparametric optimization. First, variational modal decomposition is used for noise reduction processing to maximize the retention of the original information of capacity degradation. Second, the capacity declining trend after noise reduction is modeled and predicted by the combination of bi-directional long-short term memory and temporal attention mechanism. In addition, a Bayesian optimizer is used to adaptively adjust the hyperparameters while training the model. Finally, the model was validated on NASA and CALCE data sets, and the results show that the model can accurately predict the future trend with only the initial 12% capacity data

Proteomic characterization of royal jelly proteins in Chinese (Apis cerana cerana) and European (Apis mellifera) honeybees

In this study, the proteins contained in royal jelly (RJ) derived from Chinese and European honeybees have been analyzed in detail and compared. Remarkable differences were found in the heterogeneity of major royal jelly proteins (MRJPs), MRJP2 and MRJP3, in terms of molecular weight and isoelectric points between the two species of RJ. MRJP2 and MRJP3 produced by Chinese honeybee are less polymorphic than those produced by European honeybee. This study is a contribution to the description of the royal jelly proteome

Research on the coordinated development of tourism-urbanization-ecological environment——Taking Chongqing as an example

The article analyzes the comprehensive development levels of the three subsystems of Chongqing's tourism, urbanization and ecological environment from 2000 to 2017, and the temporal evolution characteristics of the coordinated development of the three. The results show that: (1) The comprehensive evaluation index of Chongqing's tourism industry system and urbanization system is on the rise; the comprehensive ecological environment index showed an upward trend before 2008, and fluctuated greatly after 2008. (2) The degree of coupling and coordination of the three systems in Chongqing City generally shows a transformational trend of maladjustment recession-intermediate transition-coordination improvement. In the future, Chongqing should continue to deepen institutional reforms and continue to develop new tourism markets; improve transportation, promote enterprise transformation and upgrading, implement policies for talent introduction; strengthen the construction of environmental protection talents and capital investment, and vigorously develop ecological industrie