Intuitive and Efficient Roof Modeling for Reconstruction and Synthesis

We propose a novel and flexible roof modeling approach that can be used for constructing planar 3D polygon roof meshes. Our method uses a graph structure to encode roof topology and enforces the roof validity by optimizing a simple but effective planarity metric we propose. This approach is significantly more efficient than using general purpose 3D modeling tools such as 3ds Max or SketchUp, and more powerful and expressive than specialized tools such as the straight skeleton. Our optimization-based formulation is also flexible and can accommodate different styles and user preferences for roof modeling. We showcase two applications. The first application is an interactive roof editing framework that can be used for roof design or roof reconstruction from aerial images. We highlight the efficiency and generality of our approach by constructing a mesh-image paired dataset consisting of 2539 roofs. Our second application is a generative model to synthesize new roof meshes from scratch. We use our novel dataset to combine machine learning and our roof optimization techniques, by using transformers and graph convolutional networks to model roof topology, and our roof optimization methods to enforce the planarity constraint.

Cooperative Multi-Agent Transfer Learning with Level-Adaptive Credit Assignment

Extending transfer learning to cooperative multi-agent reinforcement learning (MARL) has recently received much attention. In contrast to the single-agent setting, the coordination indispensable in cooperative MARL constrains each agents policy. However, existing transfer methods focus exclusively on agent policy and ignores coordination knowledge. We propose a new architecture that realizes robust coordination knowledge transfer through appropriate decomposition of the overall coordination into several coordination patterns. We use a novel mixing network named level-adaptive QTransformer (LA-QTransformer) to realize agent coordination that considers credit assignment, with appropriate coordination patterns for different agents realized by a novel level-adaptive Transformer (LA-Transformer) dedicated to the transfer of coordination knowledge. In addition, we use a novel agent network named Population Invariant agent with Transformer (PIT) to realize the coordination transfer in more varieties of scenarios. Extensive experiments in StarCraft II micro-management show that LA-QTransformer together with PIT achieves superior performance compared with state-of-the-art baselines.

Sparse Attention with Linear Units

Recently, it has been argued that encoder-decoder models can be made more interpretable by replacing the softmax function in the attention with its sparse variants. In this work, we introduce a novel, simple method for achieving sparsity in attention: we replace the softmax activation with a ReLU, and show that sparsity naturally emerges from such a formulation. Training stability is achieved with layer normalization with either a specialized initialization or an additional gating function. Our model, which we call Rectified Linear Attention (ReLA), is easy to implement and more efficient than previously proposed sparse attention mechanisms. We apply ReLA to the Transformer and conduct experiments on five machine translation tasks. ReLA achieves translation performance comparable to several strong baselines, with training and decoding speed similar to that of the vanilla attention. Our analysis shows that ReLA delivers high sparsity rate and head diversity, and the induced cross attention achieves better accuracy with respect to source-target word alignment than recent sparsified softmax-based models. Intriguingly, ReLA heads also learn to attend to nothing (i.e. switch off) for some queries, which is not possible with sparsified softmax alternatives.

A 16-pixel NbN nanowire single photon detector coupled with 300 micrometer fiber

Niobium Nitride (NbN) nanowire is the most popular detection material of superconducting nanowire single photon detectors (SNSPDs) for high repetition rate and high efficiency. However, it has been assumed to be difficult for fabricating SNSPDs with arrays over large area, which are critical components of quantum imaging, linear optical quantum computing, satellite laser ranging, and high-speed quantum key distribution. This paper reported a 4*4 pixel NbN SNSPDs array with an equivalent receiving aperture of 300 micrometer associated with beam compression technology, which is the first NbN SNSPD coupled by 300 micrometer fiber according to our knowledge. The designed pixel was compact with a filling factor of 98.5%, resulting in a high quantum efficiency of 94.5%, a system efficiency of 46% for photons coupled from 300 micrometer fiber without optimizing polarization, and a system time resolution of 92 ps. This work demonstrates the feasibility of a large SNSPDs array, and paves the way for developing efficient photon camera with NbN nanowires.

Data Acquisition and Control System for Broad-band Microwave Reflectometry on EAST

Microwave reflectometry is a non-intrusive plasma diagnostic tool which is widely applied in many fusion devices. In 2014, the microwave reflectometry on Experimental Advanced Superconducting Tokamak (EAST) had been upgraded to measure plasma density profile and fluctuation, which covered the frequency range of Q-band (32-56 GHz), V-band (47-76 GHz) and W-band (71-110 GHz). This paper presented a dedicated data acquisition and control system (DAQC) to meet the measurement requirements of high accuracy and temporal resolution. The DAQC consisted of two control modules, which integrated arbitrary waveform generation block (AWG) and trigger processing block (TP), and two data acquisition modules (DAQ) that was implemented base on the PXIe platform from National Instruments (NI). All the performance parameters had satisfied the requirements of reflectometry. The actual performance will be further examined in the experiments of EAST in 2014.

Fidelity susceptibility and geometric phase in critical phenomenon

Motivated by recent development in quantum fidelity and fidelity susceptibility, we study relations among Lie algebra, fidelity susceptibility and quantum phase transition for a two-state system and the Lipkin-Meshkov-Glick model. We get the fidelity susceptibility for SU(2) and SU(1,1) algebraic structure models. From this relation, the validity of the fidelity susceptibility to signal for the quantum phase transition is also verified in these two systems. At the same time, we obtain the geometric phase in these two systems in the process of calculating the fidelity susceptibility. In addition, the new method of calculating fidelity susceptibility has been applied to explore the two-dimensional XXZ model and the Bose-Einstein condensate(BEC).