PERAC audit report : Revere Contributory Retirement System : Jan. 1, 2004-Dec. 31, 2005

Overall view of the north side (front of the monument) looking towards the Mausoleum of Mao Zedong; A ten-story obelisk (stele) that was erected as a national monument of the People's Republic of China to the martyrs of revolutionary struggle during the 19th and 20th centuries. It is located in the southern edge of Tiananmen Square, to the north of Mausoleum of Mao Zedong. Commissioned by the government in 1949, it was completed in 1958. The architect of the monument was Liang Sicheng, with some elements designed by his wife, Lin Huiyin (an architect and the aunt of Maya Lin). The monument has also served as the center of large-scale mourning activities that later developed into protest and unrest, such as the deaths of Premier Zhou Enlai (which developed into the Tiananmen Square protests of 1976) and Hu Yaobang (which developed into the Tiananmen Square protests of 1989). The monument covers an area of 32,000 square feet. Source: Wikipedia; http://en.wikipedia.org/wiki/Main_Page (accessed 4/26/2013

From 2d to 3d: Evolution of Object Detection Algorithms and Their Impact on Traffic Systems

Target detection is a fundamental research area in computer vision, with wide applications in pedestrian detection, vehicle recognition, and autonomous driving. For autonomous systems to navigate and engage with their surroundings safely, they must be able to detect and classify objects accurately in real-time. The development of deep learning technology has led to a rapid evolution in object detection techniques. This paper reviews the evolution of 2D object detection algorithms, ranging from deep learning-based techniques to conventional machine vision approaches. Additionally, it talks about how 3D object identification systems have advanced and how they are used in traffic situations. The study also examines the difficulties that object detection algorithms are currently facing and suggests possible avenues for further investigation. This study seeks to deliver an extensive review of the development and present landscape of object detection technologies, helping researchers better understand the key technical pathways, identify existing challenges, and explore innovative approaches for advancing the field further. It is hoped that this review can serve as a reference and inspiration for subsequent research and practical applications

Spectrum Focused Frequency Adversarial Attacks for Automatic Modulation Classification

Artificial intelligence (AI) technology has provided a potential solution for automatic modulation recognition (AMC). Unfortunately, AI-based AMC models are vulnerable to adversarial examples, which seriously threatens the efficient, secure and trusted application of AI in AMC. This issue has attracted the attention of researchers. Various studies on adversarial attacks and defenses evolve in a spiral. However, the existing adversarial attack methods are all designed in the time domain. They introduce more high-frequency components in the frequency domain, due to abrupt updates in the time domain. For this issue, from the perspective of frequency domain, we propose a spectrum focused frequency adversarial attacks (SFFAA) for AMC model, and further draw on the idea of meta-learning, propose a Meta-SFFAA algorithm to improve the transferability in the black-box attacks. Extensive experiments, qualitative and quantitative metrics demonstrate that the proposed algorithm can concentrate the adversarial energy on the spectrum where the signal is located, significantly improve the adversarial attack performance while maintaining the concealment in the frequency domain.Comment: 6 pages, 9 figure

Synthesis of the highly branched hexasaccharide core of chlorella virus N-linked glycans

Chlorella viruses produce N-linked glycoproteins with carbohydrate moieties that differ in structure from all other N- linked glycans. In addition, unlike most viruses, these organisms do not hijack the biosynthetic machinery of the host to make glycocoproteins; instead, they produce their own carbohydrate- processing enzymes. A better understanding of the function and assembly of these fascinating and structurally-unprecedented glycans requires access to probe molecules. We describe here the first synthesis of a chlorella virus N-linked glycan, a highly branched hexasaccharide that contains the pentasaccharide present in all of the >15 structures reported to date. The target molecule includes a glucosyl-asparagine linkage and a ‘hyperbranched’ fucose residue in which all of the hydroxyl groups are glycosylated. Both convergent and linear approaches were investigated with the latter being successful in providing the target in 16 steps and 13% overall yield

An oxidation–amidation approach for the synthesis of glycuronamides

A route for the synthesis of glycuronamides via the intermediacy of 6-S-tolyl-glycosides and uronic acid thioesters, is reported. The route, which is compatible with a variety of carbohydrate residues and protecting groups, was used to synthesize the repeating unit of the E. coli K54 capsular polysaccharide

FreeControl: Training-Free Spatial Control of Any Text-to-Image Diffusion Model with Any Condition

Recent approaches such as ControlNet offer users fine-grained spatial control over text-to-image (T2I) diffusion models. However, auxiliary modules have to be trained for each type of spatial condition, model architecture, and checkpoint, putting them at odds with the diverse intents and preferences a human designer would like to convey to the AI models during the content creation process. In this work, we present FreeControl, a training-free approach for controllable T2I generation that supports multiple conditions, architectures, and checkpoints simultaneously. FreeControl designs structure guidance to facilitate the structure alignment with a guidance image, and appearance guidance to enable the appearance sharing between images generated using the same seed. Extensive qualitative and quantitative experiments demonstrate the superior performance of FreeControl across a variety of pre-trained T2I models. In particular, FreeControl facilitates convenient training-free control over many different architectures and checkpoints, allows the challenging input conditions on which most of the existing training-free methods fail, and achieves competitive synthesis quality with training-based approaches.Comment: Project Page: https://genforce.github.io/freecontrol

Electromagnetic signal modulation recognition technology based on lightweight deep neural network

In response to the trend that in the 6th generation wireless (6G) era,mobile communications and artificial intelligence will be closely integrated,and a huge number of edge intelligent signal processing nodes will be deployed,an efficient and intelligent electromagnetic signal recognition model was proposed,which could be deployed on resource-constrained edge devices.The constellation diagram of electromagnetic signal was firstly drawn to visualize electromagnetic signal as a two-dimensional image,and color the constellation diagram according to the normalized point density to achieve feature enhancement.Then,a binary deep neural network was adopted to recognize the colored constellation diagrams.It was shown that the approach can guarantee a high recognition accuracy,which significantly reduced the model storage and calculation costs.For verification,the proposed approach was applied to the problem of electromagnetic signal modulation recognition.The experiment uses eight commonly used digital modulation signals and selects additive white Gaussian noise as the channel environment.The experimental results show that the scheme can achieve a comprehensive recognition rate of 96.1% under the noise condition of -6～6 dB,while the size of the network model is only 166 KB.Further,the execution time,when executed on a Raspberry Pi 4B,is only 290 ms.Compared to a full-precision network of the same scale,the accuracy is increased by 0.6%,the model is reduced to 1 26.16 ,and the running time is reduced to 1 2.37

Objective assessment of communication speech interference effect based on feature fusion

In view of the objective assessment problem of the effect of communication speech interference, methods based on multi-measurements and multimodal fusion were proposed.First, the interfered speech was preprocessed by the endpoint detection algorithm and time warping algorithm.Then, the content of speech was extracted and performed measurement calculated with the standard speech to obtain five kinds of measure.After the fusion of five measures, random forest model was used to assessed the quality level.Finally, a neural network model based on residual structure was designed combined multimodal fusion technique, which fused the graph domain and measure domain features of the interfered speech data and performed quality level assessment.Experimental results show that the accuracy of two methods have reached more than 90%.Among them, the multimodal assessment method improves the accuracy by about 3.269% compared with the existing research methods, which proves that it has a better performance