Predicting Network Controllability Robustness: A Convolutional Neural Network Approach

Network controllability measures how well a networked system can be controlled to a target state, and its robustness reflects how well the system can maintain the controllability against malicious attacks by means of node-removals or edge-removals. The measure of network controllability is quantified by the number of external control inputs needed to recover or to retain the controllability after the occurrence of an unexpected attack. The measure of the network controllability robustness, on the other hand, is quantified by a sequence of values that record the remaining controllability of the network after a sequence of attacks. Traditionally, the controllability robustness is determined by attack simulations, which is computationally time consuming. In this paper, a method to predict the controllability robustness based on machine learning using a convolutional neural network is proposed, motivated by the observations that 1) there is no clear correlation between the topological features and the controllability robustness of a general network, 2) the adjacency matrix of a network can be regarded as a gray-scale image, and 3) the convolutional neural network technique has proved successful in image processing without human intervention. Under the new framework, a fairly large number of training data generated by simulations are used to train a convolutional neural network for predicting the controllability robustness according to the input network-adjacency matrices, without performing conventional attack simulations. Extensive experimental studies were carried out, which demonstrate that the proposed framework for predicting controllability robustness of different network configurations is accurate and reliable with very low overheads.Comment: 11 pages, 7 figure

EFFECTS OF RUNNING FATIGUE ON KNEE JOINT SYMMETRY AMONG AMATEUR RUNNERS

The purpose of this study was to reveal the effects of running fatigue on the symmetry of lower limb dynamics and kinematics parameters. 18 male amateur runners participated in this study. The marker trajectories and ground reaction forces were collected via an 8-camera VICON and Kistler 3D force platform before and after the running-induced fatigue protocol. Symmetry angles (SA) of joint moments, range of motions (ROM), and joint stiffness in three planes were calculated pre- and post-fatigue. SA of knee Extension Angle, Internal rotation, Abduction moment, coronal ROM and joint stiffness significantly increased after fatigue(

PLANTAR FORCE COMPARISONS BETWEEN THE CHASSE STEP AND ONE STEP FOOTWORK DURING TOPSPIN FOREHAND USING STATISTICAL PARAMETRIC MAPPING

The purpose of this study was to investigate the plantar force characteristics of the chasse step and one step footwork during table tennis topspin stroke using one-dimensional statistical parameter mapping (SPM 1d). Twelve national players volunteered to participate in the study. The plantar force of the right foot during the chasse step and one step backward phase (BP) and forward phase (FP) was recorded by instrumented insole systems. Paired sample T tests in SPSS 24.0 (SPSSs Inc, Chicago, IL, USA) were used to analyze peak pressure of each plantar region. For SPM analysis, the plantar force time series curves were marked as a 100% process. A paired-samples T-test in MATLAB was used to analyze differences in plantar force. One step produced a greater plantar force than the chasse step during 6.92-11.22% BP (P=0.039). The chasse step produced a greater plantar force than one step during 53.47-99.01% BP (

Knowledge-Based Prediction of Network Controllability Robustness

Network controllability robustness reflects how well a networked system can maintain its controllability against destructive attacks. Its measure is quantified by a sequence of values that record the remaining controllability of the network after a sequence of node-removal or edge-removal attacks. Traditionally, the controllability robustness is studied only for directed networks and is determined by attack simulations, which is computationally time consuming or even infeasible. In the present paper, an improved method for predicting the controllability robustness of undirected networks is developed based on machine learning using a group of convolutional neural networks (CNNs). In this scheme, a number of training data generated by simulations are used to train the group of CNNs for classification and prediction, respectively. Extensive experimental studies are carried out, which demonstrate that 1) the proposed method predicts more precisely than the classical single-CNN predictor; 2) the proposed CNN-based predictor provides a better predictive measure than the traditional spectral measures and network heterogeneity.Comment: 11 pages, 8 figures in Paper; 33 pages, 2 figures in Supplementary Informatio

Improved production of docosahexaenoic acid in batch fermentation by newly-isolated strains of Schizochytrium sp. and Thraustochytriidae sp. through bioprocess optimization

Thraustochytrids, rich in docosahexaenoic acid (DHA, C22:6??3), represent a potential source of dietary fatty acids. Yet, the effect of culture conditions on growth and fatty acid composition vary widely among different thraustochytrid strains. Two different thraustochytrid strains, Schizochytrium sp. PKU#Mn4 and Thraustochytriidae sp. PKU#Mn16 were studied for their growth and DHA production characteristics under various culture conditions. Although they exhibited similar fatty acid profiles, PKU#Mn4 seemed a good candidate for industrial DHA fermentation while PKU#Mn16 displayed growth tolerance to a wide range of process conditions. Relative DHA content of 48.5% and 49.2% (relative to total fatty acids), respectively, were achieved on glycerol under their optimal flask culture conditions. Maximum DHA yield (Yp/x) of 21.0% and 18.9% and productivity of 27.6 mg/L-h and 31.9 mg/L-h were obtained, respectively, in 5-L bioreactor fermentation operated with optimal conditions and dual oxygen control strategy. A 3.4- and 2.8-fold improvement of DHA production (g/L), respectively, was achieved in this study. Overall, our study provides the potential of two thraustochytrid strains and their culture conditions for efficient production of DHA-rich oil

Deep Reinforcement Learning with Multitask Episodic Memory Based on Task-Conditioned Hypernetwork

Deep reinforcement learning algorithms are usually impeded by sampling inefficiency, heavily depending on multiple interactions with the environment to acquire accurate decision-making capabilities. In contrast, humans rely on their hippocampus to retrieve relevant information from past experiences of relevant tasks, which guides their decision-making when learning a new task, rather than exclusively depending on environmental interactions. Nevertheless, designing a hippocampus-like module for an agent to incorporate past experiences into established reinforcement learning algorithms presents two challenges. The first challenge involves selecting the most relevant past experiences for the current task, and the second challenge is integrating such experiences into the decision network. To address these challenges, we propose a novel method that utilizes a retrieval network based on task-conditioned hypernetwork, which adapts the retrieval network's parameters depending on the task. At the same time, a dynamic modification mechanism enhances the collaborative efforts between the retrieval and decision networks. We evaluate the proposed method on the MiniGrid environment.The experimental results demonstrate that our proposed method significantly outperforms strong baselines

JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models

Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents. Existing approaches can handle certain long-horizon tasks in an open world. However, they still struggle when the number of open-world tasks could potentially be infinite and lack the capability to progressively enhance task completion as game time progresses. We introduce JARVIS-1, an open-world agent that can perceive multimodal input (visual observations and human instructions), generate sophisticated plans, and perform embodied control, all within the popular yet challenging open-world Minecraft universe. Specifically, we develop JARVIS-1 on top of pre-trained multimodal language models, which map visual observations and textual instructions to plans. The plans will be ultimately dispatched to the goal-conditioned controllers. We outfit JARVIS-1 with a multimodal memory, which facilitates planning using both pre-trained knowledge and its actual game survival experiences. JARVIS-1 is the existing most general agent in Minecraft, capable of completing over 200 different tasks using control and observation space similar to humans. These tasks range from short-horizon tasks, e.g., "chopping trees" to long-horizon tasks, e.g., "obtaining a diamond pickaxe". JARVIS-1 performs exceptionally well in short-horizon tasks, achieving nearly perfect performance. In the classic long-term task of $\texttt{ObtainDiamondPickaxe}$, JARVIS-1 surpasses the reliability of current state-of-the-art agents by 5 times and can successfully complete longer-horizon and more challenging tasks. The project page is available at https://craftjarvis.org/JARVIS-1Comment: update project pag