Predictive Encoding of Contextual Relationships for Perceptual Inference, Interpolation and Prediction

We propose a new neurally-inspired model that can learn to encode the global relationship context of visual events across time and space and to use the contextual information to modulate the analysis by synthesis process in a predictive coding framework. The model learns latent contextual representations by maximizing the predictability of visual events based on local and global contextual information through both top-down and bottom-up processes. In contrast to standard predictive coding models, the prediction error in this model is used to update the contextual representation but does not alter the feedforward input for the next layer, and is thus more consistent with neurophysiological observations. We establish the computational feasibility of this model by demonstrating its ability in several aspects. We show that our model can outperform state-of-art performances of gated Boltzmann machines (GBM) in estimation of contextual information. Our model can also interpolate missing events or predict future events in image sequences while simultaneously estimating contextual information. We show it achieves state-of-art performances in terms of prediction accuracy in a variety of tasks and possesses the ability to interpolate missing frames, a function that is lacking in GBM

Weakly Secure Summation with Colluding Users

In secure summation, $K$ users, each holds an input, wish to compute the sum of the inputs at a server without revealing any information about {\em all the inputs} even if the server may collude with {\em an arbitrary subset of users}. In this work, we relax the security and colluding constraints, where the set of inputs whose information is prohibited from leakage is from a predetermined collection of sets (e.g., any set of up to $S$ inputs) and the set of colluding users is from another predetermined collection of sets (e.g., any set of up to $T$ users). For arbitrary collection of security input sets and colluding user sets, we characterize the optimal randomness assumption, i.e., the minimum number of key bits that need to be held by the users, per input bit, for weakly secure summation to be feasible, which generally involves solving a linear program.Comment: 22 pages, 1 figur

Integrated Chassis Control of Active Front Steering and Yaw Stability Control Based on Improved Inverse Nyquist Array Method

An integrated chassis control (ICC) system with active front steering (AFS) and yaw stability control (YSC) is introduced in this paper. The proposed ICC algorithm uses the improved Inverse Nyquist Array (INA) method based on a 2-degree-of-freedom (DOF) planar vehicle reference model to decouple the plant dynamics under different frequency bands, and the change of velocity and cornering stiffness were considered to calculate the analytical solution in the precompensator design so that the INA based algorithm runs well and fast on the nonlinear vehicle system. The stability of the system is guaranteed by dynamic compensator together with a proposed PI feedback controller. After the response analysis of the system on frequency domain and time domain, simulations under step steering maneuver were carried out using a 2-DOF vehicle model and a 14-DOF vehicle model by Matlab/Simulink. The results show that the system is decoupled and the vehicle handling and stability performance are significantly improved by the proposed method

Triple Regression for Camera Agnostic Sim2Real Robot Grasping and Manipulation Tasks

Sim2Real (Simulation to Reality) techniques have gained prominence in robotic manipulation and motion planning due to their ability to enhance success rates by enabling agents to test and evaluate various policies and trajectories. In this paper, we investigate the advantages of integrating Sim2Real into robotic frameworks. We introduce the Triple Regression Sim2Real framework, which constructs a real-time digital twin. This twin serves as a replica of reality to simulate and evaluate multiple plans before their execution in real-world scenarios. Our triple regression approach addresses the reality gap by: (1) mitigating projection errors between real and simulated camera perspectives through the first two regression models, and (2) detecting discrepancies in robot control using the third regression model. Experiments on 6-DoF grasp and manipulation tasks (where the gripper can approach from any direction) highlight the effectiveness of our framework. Remarkably, with only RGB input images, our method achieves state-of-the-art success rates. This research advances efficient robot training methods and sets the stage for rapid advancements in robotics and automation

Materialism as A Cultural Medium Three Projects by Finnish Architects in China

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Carbon Trading in BRICS Countries: Challenges and Recommendations

As one of the world’s largest emerging economies, BRICS countries are playing an increasingly important role in addressing the global issue of climate change. To achieve their emissions reduction targets, these nations are actively promoting the construction of carbon trading markets. However, they face multiple challenges and obstacles in this endeavor, including issues related to market norms, financial support, technical capacity, social participation, and development needs. This research investigates the problems and challenges faced by BRICS countries in terms of building carbon trading markets through literature reviews and case studies. To address these challenges, this research strengthening international cooperation and technical support, improving market norms and provide following recommendations: conducting regulatory measures, enhancing social participation and communication, and balancing the relationship between economic development and environmental protection requirements. Furthermore, it is crucial for these nations to continue to strengthen international cooperation and collaboration, working together to promote the construction of carbon trading markets, achieving their emissions reduction targets, and ensuring long-term sustainability and economic development