Optimized Decimation of Tensor Networks with Super-orthogonalization for Two-Dimensional Quantum Lattice Models

A novel algorithm based on the optimized decimation of tensor networks with super-orthogonalization (ODTNS) that can be applied to simulate efficiently and accurately not only the thermodynamic but also the ground state properties of two-dimensional (2D) quantum lattice models is proposed. By transforming the 2D quantum model into a three-dimensional (3D) closed tensor network (TN) comprised of the tensor product density operator and a 3D brick-wall TN, the free energy of the system can be calculated with the imaginary time evolution, in which the network Tucker decomposition is suggested for the first time to obtain the optimal lower-dimensional approximation on the bond space by transforming the TN into a super-orthogonal form. The efficiency and accuracy of this algorithm are testified, which are fairly comparable with the quantum Monte Carlo calculations. Besides, the present ODTNS scheme can also be applicable to the 2D frustrated quantum spin models with nice efficiency

Towards Efficient and Scalable Acceleration of Online Decision Tree Learning on FPGA

Decision trees are machine learning models commonly used in various application scenarios. In the era of big data, traditional decision tree induction algorithms are not suitable for learning large-scale datasets due to their stringent data storage requirement. Online decision tree learning algorithms have been devised to tackle this problem by concurrently training with incoming samples and providing inference results. However, even the most up-to-date online tree learning algorithms still suffer from either high memory usage or high computational intensity with dependency and long latency, making them challenging to implement in hardware. To overcome these difficulties, we introduce a new quantile-based algorithm to improve the induction of the Hoeffding tree, one of the state-of-the-art online learning models. The proposed algorithm is light-weight in terms of both memory and computational demand, while still maintaining high generalization ability. A series of optimization techniques dedicated to the proposed algorithm have been investigated from the hardware perspective, including coarse-grained and fine-grained parallelism, dynamic and memory-based resource sharing, pipelining with data forwarding. We further present a high-performance, hardware-efficient and scalable online decision tree learning system on a field-programmable gate array (FPGA) with system-level optimization techniques. Experimental results show that our proposed algorithm outperforms the state-of-the-art Hoeffding tree learning method, leading to 0.05% to 12.3% improvement in inference accuracy. Real implementation of the complete learning system on the FPGA demonstrates a 384x to 1581x speedup in execution time over the state-of-the-art design.Comment: appear as a conference paper in FCCM 201

Balancing Food Security and Environmental Sustainability by Optimizing Seasonal-Spatial Crop Production in Bangladesh

The intensification of crop production has been identified as one of the major drivers of environmental degradation. While significant advances could still be made with more widespread adoption of sustainable intensification technologies that address the agronomic efficiency of nitrogen fertilizers, the dynamic use of agricultural land across seasons and associated crop-specific responses to fertilizer applications have so far been largely overlooked. This paper explores the potential for improving the economic-environmental performance of crop production through spatially integrated modeling and optimization, as applied to Bangladesh. Results show that per-billion-Taka nitrogen loss from soil would decline by 83% from the baseline level through factoring in crop-specific, seasonal and spatial variations in crop nitrogen-use efficiency and nitrogen transport. The approach should complement other policy analysis and decision-support tools to assess alternative options for maximizing the positive outcomes of nitrogen fertilizers with regard to farm income and food security, while maintaining environmental sustainability

Mani-GPT: A Generative Model for Interactive Robotic Manipulation

In real-world scenarios, human dialogues are multi-round and diverse. Furthermore, human instructions can be unclear and human responses are unrestricted. Interactive robots face difficulties in understanding human intents and generating suitable strategies for assisting individuals through manipulation. In this article, we propose Mani-GPT, a Generative Pre-trained Transformer (GPT) for interactive robotic manipulation. The proposed model has the ability to understand the environment through object information, understand human intent through dialogues, generate natural language responses to human input, and generate appropriate manipulation plans to assist the human. This makes the human-robot interaction more natural and humanized. In our experiment, Mani-GPT outperforms existing algorithms with an accuracy of 84.6% in intent recognition and decision-making for actions. Furthermore, it demonstrates satisfying performance in real-world dialogue tests with users, achieving an average response accuracy of 70%

DIRECT TORQUE CONTROL OF AC ELECTRIC MACHINES

This disclosure features an apparatus including a motor controller to generate control signals to control an electric motor. The motor controller includes a first saturation controller to generate a first saturation controller output based on feedback signals associated with the electric motor. The motor controller further includes a duty ratio modulator coupled to the first saturation controller. The duty ratio modulator is configured to determine activation times for a set of voltage vectors based on the first saturation controller output. The motor controller is configured to generate, at each switching cycle, a control signal based on the set of voltage vectors and the activation times for the set of voltage vectors, and provide the control signal for controlling the electric motor