A Distributed Technique for Localization of Agent Formations from Relative Range Measurements

Autonomous agents deployed or moving on land for the purpose of carrying out coordinated tasks need to have good knowledge of their absolute or relative position. For large formations it is often impractical to equip each agent with an absolute sensor such as GPS, whereas relative range sensors measuring inter-agent distances are cheap and commonly available. In this setting, the paper considers the problem of autonomous, distributed estimation of the position of each agent in a networked formation, using noisy measurements of inter- agent distances. The underlying geometrical problem has been studied quite extensively in various fields, ranging from molecular biology to robotics, and it is known to lead to a hard non-convex optimization problem. Centralized algorithms do exist that work reasonably well in finding local or global minimizers for this problem (e.g. semidefinite programming relaxations). Here, we explore a fully decentralized approach for localization from range measurements, and we propose a computational scheme based on a distributed gradient algorithm with Barzilai-Borwein stepsizes. The advantage of this distributed approach is that each agent may autonomously compute its position estimate, exchanging information only with its neighbors, without need of communicating with a central station and without needing complete knowledge of the network structur

A genome-wide transcriptome profiling reveals the early molecular events during callus initiation in Arabidopsis multiple organs

AbstractInduction of a pluripotent cell mass termed callus is the first step in an in vitro plant regeneration system, which is required for subsequent regeneration of new organs or whole plants. However, the early molecular mechanism underlying callus initiation is largely elusive. Here, we analyzed the dynamic transcriptome profiling of callus initiation in Arabidopsis aerial and root explants and identified 1342 differentially expressed genes in both explants after incubation on callus-inducing medium. Detailed categorization revealed that the differentially expressed genes were mainly related to hormone homeostasis and signaling, transcriptional and post transcriptional regulations, protein phosphorelay cascades and DNA- or chromatin-modification. Further characterization showed that overexpression of two transcription factors, HB52 or CRF3, resulted in the callus formation in transgenic plants without exogenous auxin. Therefore, our comprehensive analyses provide some insight into the early molecular regulations during callus initiation and are useful for further identification of the regulators governing callus formation

Development of Large-And-Small Volume-Switching Rotary Compressor for Residential Multi-Connected Air-Conditioner

Residential Multi-Connected Air-Conditioner(RMAC) are widely used in urban residential in China, but they have low energy efficiency problems in daily use. They are mainly due to the air-conditioning habits of Chinese residents in part space and part time , Which makes RMAC most of the time only 1-2 indoor units are turned on, and 60% of the operating time is at a low load rate of less than 30%. During low load operation, the compressor\u27s efficiency drops sharply, and there is also the problem of frequent start and stop when the minimum output is too large. In order to solve this problem, a large-and-small volume switching compressor suitable for RMAC is developed. The compressor can choose single-cylinder small-volume operation or twin-cylinder large-volume operation according to the change of system load. The new compressor technology for RMAC improves the energy efficiency by 124% under 10% load, and avoids the problem of frequent start-stops at low loads, making RMAC more energy efficient and more comfortable

Research Issues, Challenges, and Opportunities of Wireless Power Transfer-Aided Full-Duplex Relay Systems

We present a comprehensive review for wireless power transfer (WPT)-aided full-duplex (FD) relay systems. Two critical challenges in implementing WPT-aided FD relay systems are presented, that is, pseudo FD realization and high power consumption. Existing time-splitting or power-splitting structure based-WPT-aided FD relay systems can only realize FD operation in one of the time slots or only forward part of the received signal to the destination, belonging to pseudo FD realization. Besides, self-interference is treated as noise and self-interference cancellation (SIC) operation incurs high power consumption at the FD relay node. To this end, a promising solution is outlined to address the two challenges, which realizes consecutive FD realization at all times and forwards all the desired signal to the destination for decoding. Also, active SIC, that is, analog/digital cancellation, is not required by the proposed solution, which effectively reduces the circuit complexity and releases high power consumption at the FD relay node. Specific classifications and performance metrics of WPT-aided FD relay systems are summarized. Some future research is also envisaged for WPT-aided FD systems

Accelerated partial separable model using dimension-reduced optimization technique for ultra-fast cardiac MRI

Objective. Imaging dynamic object with high temporal resolution is challenging in magnetic resonance imaging (MRI). Partial separable (PS) model was proposed to improve the imaging quality by reducing the degrees of freedom of the inverse problem. However, PS model still suffers from long acquisition time and even longer reconstruction time. The main objective of this study is to accelerate the PS model, shorten the time required for acquisition and reconstruction, and maintain good image quality simultaneously. Approach. We proposed to fully exploit the dimension reduction property of the PS model, which means implementing the optimization algorithm in subspace. We optimized the data consistency term, and used a Tikhonov regularization term based on the Frobenius norm of temporal difference. The proposed dimension-reduced optimization technique was validated in free-running cardiac MRI. We have performed both retrospective experiments on public dataset and prospective experiments on in-vivo data. The proposed method was compared with four competing algorithms based on PS model, and two non-PS model methods. Main results. The proposed method has robust performance against shortened acquisition time or suboptimal hyper-parameter settings, and achieves superior image quality over all other competing algorithms. The proposed method is 20-fold faster than the widely accepted PS+Sparse method, enabling image reconstruction to be finished in just a few seconds. Significance. Accelerated PS model has the potential to save much time for clinical dynamic MRI examination, and is promising for real-time MRI applications.Comment: 23 pages, 11 figures. Accepted as manuscript on Physics in Medicine & Biolog

Forward and Backward Information Retention for Accurate Binary Neural Networks

Weight and activation binarization is an effective approach to deep neural network compression and can accelerate the inference by leveraging bitwise operations. Although many binarization methods have improved the accuracy of the model by minimizing the quantization error in forward propagation, there remains a noticeable performance gap between the binarized model and the full-precision one. Our empirical study indicates that the quantization brings information loss in both forward and backward propagation, which is the bottleneck of training accurate binary neural networks. To address these issues, we propose an Information Retention Network (IR-Net) to retain the information that consists in the forward activations and backward gradients. IR-Net mainly relies on two technical contributions: (1) Libra Parameter Binarization (Libra-PB): simultaneously minimizing both quantization error and information loss of parameters by balanced and standardized weights in forward propagation; (2) Error Decay Estimator (EDE): minimizing the information loss of gradients by gradually approximating the sign function in backward propagation, jointly considering the updating ability and accurate gradients. We are the first to investigate both forward and backward processes of binary networks from the unified information perspective, which provides new insight into the mechanism of network binarization. Comprehensive experiments with various network structures on CIFAR-10 and ImageNet datasets manifest that the proposed IR-Net can consistently outperform state-of-the-art quantization methods

Exploring the Ligand-Protein Networks in Traditional Chinese Medicine: Current Databases, Methods, and Applications

The traditional Chinese medicine (TCM), which has thousands of years of clinical application among China and other Asian countries, is the pioneer of the "multicomponent-multitarget" and network pharmacology. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This paper firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in detail along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM

Electrophysiological correlates of the perceptual fluency effect on recognition memory in different fluency contexts

The present study used event-related potentials (ERPs) to investigate the contribution of perceptual fluency to recognition memory in different fluency contexts. In a recognition memory test with a modified remember-know paradigm, we employed conceptually impoverished items (kaleidoscope images) as stimuli and manipulated the perceptual fluency of recognition test cues through masked repetition priming. There were two fluency context conditions. In the random fluency context (RC) condition, primed and unprimed trials were randomly inter-mixed. In the blocked fluency context (BC) condition, primed and unprimed trials were grouped into blocks. Behavioral results showed that priming elevated the incidence of remember hits and the accuracy of remember judgements in the RC condition; no such effects were evident in the BC condition. In addition, priming effects on reaction times were found only for remember hit responses in the RC condition. The ERP results revealed an early100-200 ms effect related to masked repetition priming, which took the form of greater positivity for primed than unprimed trials. This effect was modulated neither by fluency context or response type. The present findings suggest that perceptual fluency induced by masked repetition priming affects recollection-related memory judgments in a specific fluency context and indicate that relative, rather absolute, fluency plays a critical role in influencing recognition memory judgments