DecideNet: Counting Varying Density Crowds Through Attention Guided Detection and Density Estimation

In real-world crowd counting applications, the crowd densities vary greatly in spatial and temporal domains. A detection based counting method will estimate crowds accurately in low density scenes, while its reliability in congested areas is downgraded. A regression based approach, on the other hand, captures the general density information in crowded regions. Without knowing the location of each person, it tends to overestimate the count in low density areas. Thus, exclusively using either one of them is not sufficient to handle all kinds of scenes with varying densities. To address this issue, a novel end-to-end crowd counting framework, named DecideNet (DEteCtIon and Density Estimation Network) is proposed. It can adaptively decide the appropriate counting mode for different locations on the image based on its real density conditions. DecideNet starts with estimating the crowd density by generating detection and regression based density maps separately. To capture inevitable variation in densities, it incorporates an attention module, meant to adaptively assess the reliability of the two types of estimations. The final crowd counts are obtained with the guidance of the attention module to adopt suitable estimations from the two kinds of density maps. Experimental results show that our method achieves state-of-the-art performance on three challenging crowd counting datasets.Comment: CVPR 201

Graph Condensation via Eigenbasis Matching

The increasing amount of graph data places requirements on the efficiency and scalability of graph neural networks (GNNs), despite their effectiveness in various graph-related applications. Recently, the emerging graph condensation (GC) sheds light on reducing the computational cost of GNNs from a data perspective. It aims to replace the real large graph with a significantly smaller synthetic graph so that GNNs trained on both graphs exhibit comparable performance. However, our empirical investigation reveals that existing GC methods suffer from poor generalization, i.e., different GNNs trained on the same synthetic graph have obvious performance gaps. What factors hinder the generalization of GC and how can we mitigate it? To answer this question, we commence with a detailed analysis and observe that GNNs will inject spectrum bias into the synthetic graph, resulting in a distribution shift. To tackle this issue, we propose eigenbasis matching for spectrum-free graph condensation, named GCEM, which has two key steps: First, GCEM matches the eigenbasis of the real and synthetic graphs, rather than the graph structure, which eliminates the spectrum bias of GNNs. Subsequently, GCEM leverages the spectrum of the real graph and the synthetic eigenbasis to construct the synthetic graph, thereby preserving the essential structural information. We theoretically demonstrate that the synthetic graph generated by GCEM maintains the spectral similarity, i.e., total variation, of the real graph. Extensive experiments conducted on five graph datasets verify that GCEM not only achieves state-of-the-art performance over baselines but also significantly narrows the performance gaps between different GNNs.Comment: Under Revie

Hydraulic motor with swinging planetary drive of bevel gears and its force & efficiency calculation

A new kind of hydraulic motor with offset swining planetary drive of bevel gears and the application in Capstan is presented; the researched of the calculation of the efficiency of the mechanism is made. Compared to the former structure, new kind of structure has many advantages over that of the old structure. The hydraulic driving system in the traditional derrick car is: hydraulic motor drives a reducer, and then the reducer in turn drives a capstan. Another hydraulic driving system is: an axial piston motor and an internal cycloid pinwheel drive are applied, so that the reducer and the capstan are combined into one so that simplified the transmission system. In the third version, a hydraulic motor is applied, which can be considered as the combination of hydraulic motor with axial piston and the planetary reducer and then drives the capstan, so that simplified the transmission system as well. The new kind of hydraulic motor with offset swing planetary drive of bevel gears, which combined the hydraulic motor, the internal planetary reducer with bevel gears, and the capstan together, is the most compact structure. It is of more advantage in the occasion when space is extremely limited. In the paper, two kind of construction of this mechanism is presented, and the analysis of the efficiency of this kind of transmission is made as well

Highly effective way in five-axis sculptured surfaces machining using flat-end cutter

This paper applied the concept of “contact” in Differential Geometry into the machining of the sculptured surface. I presented the contact principle of the machining of complicated surfaces, using the circumference circle of the cylindrical cutter to sweep the curved surface instead of ball-end mill. This is highly effective method. In this paper an theory for machining complicated surface is presented. By using a flat-end mill instead of ball-end mill, and adjusting the axis relate to the surface, the two surfaces, The swept surface and the required surface, has the same curvature, up to as high as 3th order

The second fundamental law of gearing and contact stress calculation of high order contact gearing

After an extensive research on the fundamental theory, the theoretical basis for the Logix gearing will be presented in this paper. This includes the theory for gear meshing with high degree of contact. In comparison to previous studies, the theory of this paper is more restricted going beyond third order parameters into the fourth order, the teeth profile had one order of contact higher than that of the Logix gearing. So that further improved the contact strength of tooth profile. A large category of gearings with high order of contact was presented, while the Logix gear is only one of them, or the special example in realization of my theory. In the Logix gearing, the zigzag curvature center curve of media rack does not always lay about the pitch line and does not extend along the pitch line continuously and steadily. It goes forward and returns. So that, in terms of properties of the transverse engagement, the Logix gearing has not much improvement over the traditional Novikov gearing (in which the transverse engagement is temporally). In my gearing the curvature center of basic rack extend along the pitch line continuously and steadily. The transverse engagement factor is larger than that of Logix gearing

Moving Deep Learning into Web Browser: How Far Can We Go?

Recently, several JavaScript-based deep learning frameworks have emerged, making it possible to perform deep learning tasks directly in browsers. However, little is known on what and how well we can do with these frameworks for deep learning in browsers. To bridge the knowledge gap, in this paper, we conduct the first empirical study of deep learning in browsers. We survey 7 most popular JavaScript-based deep learning frameworks, investigating to what extent deep learning tasks have been supported in browsers so far. Then we measure the performance of different frameworks when running different deep learning tasks. Finally, we dig out the performance gap between deep learning in browsers and on native platforms by comparing the performance of TensorFlow.js and TensorFlow in Python. Our findings could help application developers, deep-learning framework vendors and browser vendors to improve the efficiency of deep learning in browsers

A Learnable Optimization and Regularization Approach to Massive MIMO CSI Feedback

Channel state information (CSI) plays a critical role in achieving the potential benefits of massive multiple input multiple output (MIMO) systems. In frequency division duplex (FDD) massive MIMO systems, the base station (BS) relies on sustained and accurate CSI feedback from the users. However, due to the large number of antennas and users being served in massive MIMO systems, feedback overhead can become a bottleneck. In this paper, we propose a model-driven deep learning method for CSI feedback, called learnable optimization and regularization algorithm (LORA). Instead of using l1-norm as the regularization term, a learnable regularization module is introduced in LORA to automatically adapt to the characteristics of CSI. We unfold the conventional iterative shrinkage-thresholding algorithm (ISTA) to a neural network and learn both the optimization process and regularization term by end-toend training. We show that LORA improves the CSI feedback accuracy and speed. Besides, a novel learnable quantization method and the corresponding training scheme are proposed, and it is shown that LORA can operate successfully at different bit rates, providing flexibility in terms of the CSI feedback overhead. Various realistic scenarios are considered to demonstrate the effectiveness and robustness of LORA through numerical simulations