A graphical editor for OSU v3.0

Development of graphical user interface (GUI) applications is difficult since the process can be both complicated and tedious. We propose a solution directed at reducing programming time and effort required to build a GUI application. Our solution is based on the Petri Network, the Oregon SpeedCode Universe (OSU) Application Framework, and the OSU Browser (v. 3.0). A Petri Network is a visual programming language which is used represent the sequencing of objects and messages. The Application Framework provides reusable components in the form of objects. The Browser provides a visual way to examine a system in search of reusable components. A Petri Net editor was constructed which incorporates a code generator and browser. This editor uses direct-manipulation to simplify coding tasks, accepting specifications from the developer and generating the internal representations of the Petri Net. The internal representation is input to the Code Generator, thus generating an OSU Application Framework-based C++ program as output. Using the Petri Net editor to generate four application programs ; 1) drawing program, 2) a help system, 3) a calculator, and 4) a record query system, it is estimated that programming time has been reduced by 90% and programming effort has been reduced by 79%

SpawnNet: Learning Generalizable Visuomotor Skills from Pre-trained Networks

The existing internet-scale image and video datasets cover a wide range of everyday objects and tasks, bringing the potential of learning policies that have broad generalization. Prior works have explored visual pre-training with different self-supervised objectives, but the generalization capabilities of the learned policies remain relatively unknown. In this work, we take the first step towards this challenge, focusing on how pre-trained representations can help the generalization of the learned policies. We first identify the key bottleneck in using a frozen pre-trained visual backbone for policy learning. We then propose SpawnNet, a novel two-stream architecture that learns to fuse pre-trained multi-layer representations into a separate network to learn a robust policy. Through extensive simulated and real experiments, we demonstrate significantly better categorical generalization compared to prior approaches in imitation learning settings

CCL13 and human diseases

CCL13/MCP-4 belongs to the CC chemokine family, which induces chemotaxis in many immune cells. Despite extensive research into its function in numerous disorders, a thorough analysis of CCL13 is not yet accessible. The role of CCL13 in human disorders and existing CCL13-focused therapies are outlined in this study. The function of CCL13 in rheumatic diseases, skin conditions, and cancer is comparatively well-established, and some studies also suggest that it may be involved in ocular disorders, orthopedic conditions, nasal polyps, and obesity. We also give an overview of research that found very little evidence of CCL13 in HIV, nephritis, and multiple sclerosis. Even though CCL13-mediated inflammation is frequently linked to disease pathogenesis, it’s fascinating to note that in some conditions, like primary biliary cholangitis (PBC) and suicide, it might even act as a preventative measure

Enhanced Performance of a Cascaded Receiver Consisting of a DNN-Based Waveform-to-Symbol Converter and Modified NN-Based DD-LMS in CAP Underwater VLC System

Underwater visible light communication (UVLC) based on LEDs has become a competitive candidate, which is able to provide high data rates, low latency and low cost for next-generation wireless communication technologies. However, it is still challenging to achieve high-speed communication because of bottleneck problems such as bandwidth limitation and linear and nonlinear distortions. Traditional Deep-learning Neural Network (DNN)-based waveform-to-symbol converter is verified to be an effective method to alleviate them, but impractical due to high complexity. To achieve a better tradeoff between communication performance and computation complexity, a cascaded receiver consisting of a DNN-based waveform-to-symbol converter and modified Neural Network (NN)-based decision-directed least mean square (DD-LMS) is then innovatively proposed. With fewer taps and nodes than the traditional converter, the front-stage converter could mitigate the majority of Inter-Symbol Interference (ISI) and signal nonlinear distortions. Then modified NN-based DD-LMS is cascaded to improve communication performance by reducing phase offset, making received constellation points more concentrated and closer to standard constellation points. Compared with the traditional converter, the cascaded receiver could achieve 89.6% of signal Vpp dynamic range with 12.4% of complexity in the 64APSK UVLC system. Moreover, the ratio of signal Vpp dynamic range and total trainable parameters is 1.24 × 10−1 mV, while that of the traditional converter is 1.95 × 10−2 mV. The cascaded receiver used in 64APSK UVLC systems is experimentally verified to achieve enhanced performance, thus as a promising scheme for future high-speed underwater VLC

Advanced Modulation Format of Probabilistic Shaping Bit Loading for 450-nm GaN Laser Diode based Visible Light Communication

Visible light communication is an emerging high-speed optical wireless communication technology that can be a candidate to alleviate pressure on conventional radio frequency-based technology. In this paper, for the first time, the advanced modulation format of probabilistic shaping (PS) bit loading is investigated in a high data rate visible light communication system based on a 450-nm Gallium Nitride laser diode. The characteristic of the system is discussed and PS bit loading discrete multi-tone modulation helps to raise the spectral efficiency and improve the system performance. Higher entropy can be achieved in the same signal-to-noise ratio (SNR) and modulation bandwidth limitation, comparing to bit and power loading. With PS bit loading, an available information rate (AIR) of 10.23 Gbps is successfully achieved at the signal bandwidth of 1.5 GHz in a 1.2 m free space transmission with normalized generalized mutual information above 0.92. And higher AIR can be anticipated with an entropy-loading strategy that fixes the channel characteristic. Experimental results validate that a PS bit loading scheme has the potential to increase the system capacity

Investigating effects of element composition on the microstructure and mechanical properties of three types FeCrAl alloys through small punch test

This study investigated the effects of element composition on the microstructure and mechanical properties of three FeCrAl alloys (denoted as Q1, Q2, Q3). The addition of reactive elements (REs) 2 wt% Mo resulted in a 60 % refinement of grain size and an increase in kernel angle misorientation (KAM). Alloy texture was notably influenced by elemental composition, and γ texture (111)[01¯1] exhibited higher energy than (111)[12¯1]. The {110} 〈111〉 slip system exhibited greater resistance to deformation and lower susceptibility to activation than {123} 〈111〉. The yield load (Py-CEN), maximum load (Fm), and fracture energy (Esp) of the alloys at 298 K, 573 K, 723 K, 873 K, and 1023 K were evaluated through small punch test (SPT). The Fm of the three alloys were two-staged with notable temperature dependence. In this context, Q1 exhibited the lowest absolute slope of 2.97 (Q2: 3.44, Q3: 3.46) in the second stage. Furthermore, the Esp value of Q3 was the lowest at room temperature (RT, 298 K) and the highest at high temperatures (873 K and 1023 K) among the three alloys. The obtained results of SPT at different temperatures suggested a strong temperature dependence in the mechanical properties