Maximum A Posteriori Inference in Sum-Product Networks

Sum-product networks (SPNs) are a class of probabilistic graphical models that allow tractable marginal inference. However, the maximum a posteriori (MAP) inference in SPNs is NP-hard. We investigate MAP inference in SPNs from both theoretical and algorithmic perspectives. For the theoretical part, we reduce general MAP inference to its special case without evidence and hidden variables; we also show that it is NP-hard to approximate the MAP problem to $2^{n^\epsilon}$ for fixed $0 \leq \epsilon < 1$, where $n$ is the input size. For the algorithmic part, we first present an exact MAP solver that runs reasonably fast and could handle SPNs with up to 1k variables and 150k arcs in our experiments. We then present a new approximate MAP solver with a good balance between speed and accuracy, and our comprehensive experiments on real-world datasets show that it has better overall performance than existing approximate solvers

Grain-filling pattern of super hybrid rice Liangyoupeijiu under direct seeding and transplanting condition

To evaluate the grain-filling pattern, Chinese first super hybrid rice, Liangyoupeijiu was grown under tillage and establishment methods at a spacing of 20 cm × 20 cm with one seedling hill-1 and at a seeding rate of 22.5 kg ha-1 in Changsha, Hunan Province, China in 2012. Our results showed that, superior grain weight in TP had always higher than DS up to 24 DAH but at 36 DAH, grain weight had similar in both TP and DS. Middle grain weight was higher in TP than DS up to 18DAH but it was higher in DS than TP at 24 36 DAH and at 36 DAH, grain weight of DS had significantly higher than TP. Inferior grain weight was higher in TP than DS up to 12 DAH but it was higher in DS than TP at 24 -36 DAH and at 36 DAH, grain weight of DS had significantly higher than TP. Grain-filling rate of superior grain had higher in TP than DS up to 18 DAH but it was higher in DS than TP at 30 DAH. In middle grain, it was higher in TP at 6DAH but in DS, it was higher at 30 DAH. In inferior grain, it was higher in TP at 36 DAH but in DS, it was higher at 30 DAH. The heavier grain was found in TP only in superior grain but DS had heavier grain both in middle and inferior grain. Grain-filling rate of superior grain was higher in TP than DS and it was similar in both TP and DS in middle grain. But in inferior grain, it was significantly higher in DS than TP. Transplanting method produced slightly higher grain yield due to higher sink size (more number of spikelets caused by longer panicle and more number of spikelet per cm of panicle) but it was statistically similar with DS. DOI: http://dx.doi.org/10.3329/ijarit.v4i1.20972 Int. J. Agril. Res. Innov. &amp; Tech. 4 (1): 11-15, June, 201

An Efficient Deep Learning Approach Using Improved Generative Adversarial Networks for Incomplete Information Completion of Self-driving

Autonomous driving is the key technology of intelligent logistics in Industrial Internet of Things (IIoT). In autonomous driving, the appearance of incomplete point clouds losing geometric and semantic information is inevitable owing to limitations of occlusion, sensor resolution, and viewing angle when the Light Detection And Ranging (LiDAR) is applied. The emergence of incomplete point clouds, especially incomplete vehicle point clouds, would lead to the reduction of the accuracy of autonomous driving vehicles in object detection, traffic alert, and collision avoidance. Existing point cloud completion networks, such as Point Fractal Network (PF-Net), focus on the accuracy of point cloud completion, without considering the efficiency of inference process, which makes it difficult for them to be deployed for vehicle point cloud repair in autonomous driving. To address the above problem, in this paper, we propose an efficient deep learning approach to repair incomplete vehicle point cloud accurately and efficiently in autonomous driving. In the proposed method, an efficient downsampling algorithm combining incremental sampling and one-time sampling is presented to improves the inference speed of the PF-Net based on Generative Adversarial Network (GAN). To evaluate the performance of the proposed method, a real dataset is used, and an autonomous driving scene is created, where three incomplete vehicle point clouds with 5 different sizes are set for three autonomous driving situations. The improved PF-Net can achieve the speedups of over 19x with almost the same accuracy when compared to the original PF-Net. Experimental results demonstrate that the improved PF-Net can be applied to efficiently complete vehicle point clouds in autonomous driving.Comment: 10 figures, 4 table

Angular Gap: Reducing the Uncertainty of Image Difficulty through Model Calibration

Curriculum learning needs example difficulty to proceed from easy to hard. However, the credibility of image difficulty is rarely investigated, which can seriously affect the effectiveness of curricula. In this work, we propose Angular Gap, a measure of difficulty based on the difference in angular distance between feature embeddings and class-weight embeddings built by hyperspherical learning. To ascertain difficulty estimation, we introduce class-wise model calibration, as a post-training technique, to the learnt hyperbolic space. This bridges the gap between probabilistic model calibration and angular distance estimation of hyperspherical learning. We show the superiority of our calibrated Angular Gap over recent difficulty metrics on CIFAR10-H and ImageNetV2. We further propose a curriculum based on Angular Gap for unsupervised domain adaptation that can translate from learning easy samples to mining hard samples. We combine this curriculum with a state-of-the-art self-training method, Cycle Self Training (CST). The proposed Curricular CST learns robust representations and outperforms recent baselines on Office31 and VisDA 2017

Study and Prospects: Adaptive Planning and Control of Supply Chain in One-of-a-kind Production

Based on the research project titled “Adaptive Planning and Control of Supply Chain in One-of-a-kind Production”, the research group performed a systematic review of supply chain integration, risk prediction and control and trace ability. Studies of a computer-aided and integrated production system for cost-effective OKP systemare included. Our efforts relevant to integration of supply chain in OKP, modeling &control of ripple effects in OKP supply chain and the trace ability of the OKP supply chain are introduced in this paper