History of the tether concept and tether missions: a review

This paper introduces history of space tethers, including tether concepts and tether missions, and attempts to provide a source of references for historical understanding of space tethers. Several concepts of space tethers since the original concept has been conceived are listed in the literature, as well as a summary of interesting applications, and a research of space tethers is given. With the aim of implementing scientific experiments in aerospace, several space tether missions which have been delivered for aerospace application are introduced in the literature.</jats:p

Fast 2D Bicephalous Convolutional Autoencoder for Compressing 3D Time Projection Chamber Data

High-energy large-scale particle colliders produce data at high speed in the order of 1 terabytes per second in nuclear physics and petabytes per second in high-energy physics. Developing real-time data compression algorithms to reduce such data at high throughput to fit permanent storage has drawn increasing attention. Specifically, at the newly constructed sPHENIX experiment at the Relativistic Heavy Ion Collider (RHIC), a time projection chamber is used as the main tracking detector, which records particle trajectories in a volume of a three-dimensional (3D) cylinder. The resulting data are usually very sparse with occupancy around 10.8%. Such sparsity presents a challenge to conventional learning-free lossy compression algorithms, such as SZ, ZFP, and MGARD. The 3D convolutional neural network (CNN)-based approach, Bicephalous Convolutional Autoencoder (BCAE), outperforms traditional methods both in compression rate and reconstruction accuracy. BCAE can also utilize the computation power of graphical processing units suitable for deployment in a modern heterogeneous high-performance computing environment. This work introduces two BCAE variants: BCAE++ and BCAE-2D. BCAE++ achieves a 15% better compression ratio and a 77% better reconstruction accuracy measured in mean absolute error compared with BCAE. BCAE-2D treats the radial direction as the channel dimension of an image, resulting in a 3x speedup in compression throughput. In addition, we demonstrate an unbalanced autoencoder with a larger decoder can improve reconstruction accuracy without significantly sacrificing throughput. Lastly, we observe both the BCAE++ and BCAE-2D can benefit more from using half-precision mode in throughput (76-79% increase) without loss in reconstruction accuracy. The source code and links to data and pretrained models can be found at https://github.com/BNL-DAQ-LDRD/NeuralCompression_v2

Give me a hint: An ID-free small data transmission protocol for dense IoT devices

IoT (Internet of Things) has attracted a lot of attention recently. IoT devices need to report their data or status to base stations at various frequencies. The IoT communications observed by a base station normally exhibit the following characteristics: (1) massively connected, (2) lightly loaded per packet, and (3) periodical or at least mostly predictable. The current design principals of communication networks, when applied to IoT scenarios, however, do not fit well to these requirements. For example, an IPv6 address is 128 bits, which is much longer than a 16-bit temperature report. Also, contending to send a small packet is not cost-effective. In this work, we propose a novel framework, which is slot-based, schedule-oriented, and identity-free for uploading IoT devices' data. We show that it fits very well for IoT applications. The main idea is to bundle time slots with certain hashing functions of device IDs, thus significantly reducing transmission overheads, including device IDs and contention overheads. The framework is applicable from small-scale body-area (wearable) networks to large-scale massively connected IoT networks. Our simulation results verify that this framework is very effective for IoT small data uploading

A Unified Distributed Method for Constrained Networked Optimization via Saddle-Point Dynamics

This paper develops a unified distributed method for solving two classes of constrained networked optimization problems, i.e., optimal consensus problem and resource allocation problem with non-identical set constraints. We first transform these two constrained networked optimization problems into a unified saddle-point problem framework with set constraints. Subsequently, two projection-based primal-dual algorithms via Optimistic Gradient Descent Ascent (OGDA) method and Extra-gradient (EG) method are developed for solving constrained saddle-point problems. It is shown that the developed algorithms achieve exact convergence to a saddle point with an ergodic convergence rate $O(1/k)$ for general convex-concave functions. Based on the proposed primal-dual algorithms via saddle-point dynamics, we develop unified distributed algorithm design and convergence analysis for these two networked optimization problems. Finally, two numerical examples are presented to demonstrate the theoretical results

An Ontology of Chinese Radicals: Concept Derivation and Knowledge Representation based on the Semantic Symbols of Four Hoofed-Mammals

PACLIC / The University of the Philippines Visayas Cebu College Cebu City, Philippines / November 20-22, 200

VarietySound: Timbre-Controllable Video to Sound Generation via Unsupervised Information Disentanglement

Video to sound generation aims to generate realistic and natural sound given a video input. However, previous video-to-sound generation methods can only generate a random or average timbre without any controls or specializations of the generated sound timbre, leading to the problem that people cannot obtain the desired timbre under these methods sometimes. In this paper, we pose the task of generating sound with a specific timbre given a video input and a reference audio sample. To solve this task, we disentangle each target sound audio into three components: temporal information, acoustic information, and background information. We first use three encoders to encode these components respectively: 1) a temporal encoder to encode temporal information, which is fed with video frames since the input video shares the same temporal information as the original audio; 2) an acoustic encoder to encode timbre information, which takes the original audio as input and discards its temporal information by a temporal-corrupting operation; and 3) a background encoder to encode the residual or background sound, which uses the background part of the original audio as input. To make the generated result achieve better quality and temporal alignment, we also adopt a mel discriminator and a temporal discriminator for the adversarial training. Our experimental results on the VAS dataset demonstrate that our method can generate high-quality audio samples with good synchronization with events in video and high timbre similarity with the reference audio