Going Beyond Linear Mode Connectivity: The Layerwise Linear Feature Connectivity

Recent work has revealed many intriguing empirical phenomena in neural network training, despite the poorly understood and highly complex loss landscapes and training dynamics. One of these phenomena, Linear Mode Connectivity (LMC), has gained considerable attention due to the intriguing observation that different solutions can be connected by a linear path in the parameter space while maintaining near-constant training and test losses. In this work, we introduce a stronger notion of linear connectivity, Layerwise Linear Feature Connectivity (LLFC), which says that the feature maps of every layer in different trained networks are also linearly connected. We provide comprehensive empirical evidence for LLFC across a wide range of settings, demonstrating that whenever two trained networks satisfy LMC (via either spawning or permutation methods), they also satisfy LLFC in nearly all the layers. Furthermore, we delve deeper into the underlying factors contributing to LLFC, which reveal new insights into the spawning and permutation approaches. The study of LLFC transcends and advances our understanding of LMC by adopting a feature-learning perspective.Comment: 25 pages, 23 figure

The Influence Mechanism of Overseas Investment Bank Rating On Stock Fluctuation of Chinese Internet Enterprises in a Credit Crisis

Whether the efficiency information of China\u27s Internet enterprises which are listed overseas can be effectively transferred to capital market during a credit crisis, the rating information provided by investment banks should be a crucial bridge for listed firms and investors. In order to probe the influence mechanism of the rating information provided by investment bank, we choose Chinese concept stocks related to a credit crisis in the United States capital market in 2011 to do our empirical research. Our study found that, the timing of release of rating reports, target stock price and enterprise target market play significant influence on the fluctuation of stock prices, and the ranking of investment bank has played an important moderating role

A Temporal Densely Connected Recurrent Network for Event-based Human Pose Estimation

Event camera is an emerging bio-inspired vision sensors that report per-pixel brightness changes asynchronously. It holds noticeable advantage of high dynamic range, high speed response, and low power budget that enable it to best capture local motions in uncontrolled environments. This motivates us to unlock the potential of event cameras for human pose estimation, as the human pose estimation with event cameras is rarely explored. Due to the novel paradigm shift from conventional frame-based cameras, however, event signals in a time interval contain very limited information, as event cameras can only capture the moving body parts and ignores those static body parts, resulting in some parts to be incomplete or even disappeared in the time interval. This paper proposes a novel densely connected recurrent architecture to address the problem of incomplete information. By this recurrent architecture, we can explicitly model not only the sequential but also non-sequential geometric consistency across time steps to accumulate information from previous frames to recover the entire human bodies, achieving a stable and accurate human pose estimation from event data. Moreover, to better evaluate our model, we collect a large scale multimodal event-based dataset that comes with human pose annotations, which is by far the most challenging one to the best of our knowledge. The experimental results on two public datasets and our own dataset demonstrate the effectiveness and strength of our approach. Code can be available online for facilitating the future research

Finding Semantically Related Videos in Closed Collections

Modern newsroom tools offer advanced functionality for automatic and semi-automatic content collection from the web and social media sources to accompany news stories. However, the content collected in this way often tends to be unstructured and may include irrelevant items. An important step in the verification process is to organize this content, both with respect to what it shows, and with respect to its origin. This chapter presents our efforts in this direction, which resulted in two components. One aims to detect semantic concepts in video shots, to help annotation and organization of content collections. We implement a system based on deep learning, featuring a number of advances and adaptations of existing algorithms to increase performance for the task. The other component aims to detect logos in videos in order to identify their provenance. We present our progress from a keypoint-based detection system to a system based on deep learning

Unmanned Aerial System Tracking in Urban Canyon Environments Using External Vision

Unmanned aerial systems (UASs) are at the intersection of robotics and aerospace research. Their rise in popularity spurred the growth of interest in urban air mobility (UAM) across the world. UAM promises the next generation of transportation and logistics to be handled by UASs that operate closer to where people live and work. Therefore safety and security of UASs are paramount for UAM operations. Monitoring UAS traffic is especially challenging in urban canyon environments where traditional radar systems used for air traffic control (ATC) are limited by their line of sight (LOS). This thesis explores the design and preliminary results of a target tracking system for urban canyon environments based on a network of camera nodes. A network of stationary camera nodes can be deployed on a large scale to overcome the LOS issue in radar systems as well as cover considerable urban airspace. A camera node consists of a camera sensor, a beacon, a real-time kinematic (RTK) global navigation satellite system (GNSS) receiver, and an edge computing device. By leveraging high-precision RTK GNSS receivers and beacons, an automatic calibration process of the proposed system is devised to simplify the timeconsuming and tedious calibration of a traditional camera network present in motion capture (MoCap) systems. Through edge computing devices, the tracking system combines machine learning techniques and motion detection as hybrid measurement modes for potential targets. Then particle filters are used to estimate target tracks in real-time within the airspace from measurements obtained by the camera nodes. Simulation in a 40m×40m×15m tracking volume shows an estimation error within 0.5m when tracking multiple targets. Moreover, a scaled down physical test with off-the-shelf camera hardware is able to achieve tracking error within 0.3m on a micro-UAS in real time

Radar Signal Modulation Recognition Based on Sep-ResNet

With the development of signal processing technology and the use of new radar systems, signal aliasing and electronic interference have occurred in space. The electromagnetic signals have become extremely complicated in their current applications in space, causing difficult problems in terms of accurately identifying radar-modulated signals in low signal-to-noise ratio (SNR) environments. To address this problem, in this paper, we propose an intelligent recognition method that combines time–frequency (T–F) analysis and a deep neural network to identify radar modulation signals. The T–F analysis of the complex Morlet wavelet transform (CMWT) method is used to extract the characteristics of signals and obtain the T–F images. Adaptive filtering and morphological processing are used in T–F image enhancement to reduce the interference of noise on signal characteristics. A deep neural network with the channel-separable ResNet (Sep-ResNet) is used to classify enhanced T–F images. The proposed method completes high-accuracy intelligent recognition of radar-modulated signals in a low-SNR environment. When the SNR is −10 dB, the probability of successful recognition (PSR) is 93.44%

Perfluorooctanoic Acid (PFOA) Exposure in Early Life Increases Risk of Childhood Adiposity: A Meta-Analysis of Prospective Cohort Studies

Some articles have examined perfluorooctanoic acid (PFOA) exposure in early life in relation to risk of childhood adiposity. Nevertheless, the results from epidemiological studies exploring the associations remain inconsistent and contradictory. We thus conducted an analysis of data currently available to examine the association between PFOA exposure in early life and risk of childhood adiposity. The PubMed, EMBASE, and Web of Science databases were searched to identify studies that examined the impact of PFOA exposure in early life on childhood adiposity. A random-effects meta-analysis model was used to pool the statistical estimates. We identified ten prospective cohort studies comprising 6076 participants with PFOA exposure. The overall effect size (relative risk or odds ratio) for childhood overweight was 1.25 (95% confidence interval (CI): 1.04, 1.50; I2 = 40.5%). In addition, exposure to PFOA in early life increased the z-score of childhood body mass index (β = 0.10, 95% CI: 0.03, 0.17; I2 = 27.9%). Accordingly, exposure to PFOA in early life is associated with an increased risk for childhood adiposity. Further research is needed to verify these findings and to shed light on the molecular mechanism of PFOA in adiposity