Data Dissemination in Unified Dynamic Wireless Networks

We give efficient algorithms for the fundamental problems of Broadcast and Local Broadcast in dynamic wireless networks. We propose a general model of communication which captures and includes both fading models (like SINR) and graph-based models (such as quasi unit disc graphs, bounded-independence graphs, and protocol model). The only requirement is that the nodes can be embedded in a bounded growth quasi-metric, which is the weakest condition known to ensure distributed operability. Both the nodes and the links of the network are dynamic: nodes can come and go, while the signal strength on links can go up or down. The results improve some of the known bounds even in the static setting, including an optimal algorithm for local broadcasting in the SINR model, which is additionally uniform (independent of network size). An essential component is a procedure for balancing contention, which has potentially wide applicability. The results illustrate the importance of carrier sensing, a stock feature of wireless nodes today, which we encapsulate in primitives to better explore its uses and usefulness.Comment: 28 pages, 2 figure

Comercio electrónico conectando China y América Latina a través de la ruta de la seda digital

Latin America is the natural extension of the “Maritime Silk Road” and its exchanges and cooperation with China have become increasingly frequent in recent years. Under the current globale conomic backdrop, e-commerce as an emerging industry is gaining momentum and playing a bigger role in international  cooperation.  The  Belt  and  Road  top-level  design  features  policy  coordination, unimpededtrade, facilities connectivity and people-to-people bond respectively, which play a guiding role in China-LAC cooperation. Coupled with the status quo, the paper will mainly focus on these four sections so as to better analyze the China-LAC cooperation in Silk-Road E-commerce. This paper intends to analyze China-LAC e-commerce cooperation in fields of policy, trade, facilities, and people-to-people contact and study in which areas still remain room for bilateral cooperation and how can both sides better achieve win-win results under the framework of China’s “Belt and Road Initiative”.América Latina es la extensión natural de la “Ruta Marítima de la Seda” y sus intercambios y cooperación con China se han vuelto cada vez más frecuentes en los últimos años. En el contexto económico mundial actual, el comercio electrónico como industria emergente está cobrando impulso y desempeñando un papel más importante en la cooperación internacional. El diseño de alto nivel de la Franja y la Ruta presenta coordinación de políticas, comercio sin trabas, conectividad de instalaciones y vínculo entre personas, respectivamente, que desempeñan un papel rector en la cooperación entre China y ALC. Junto con el statu quo, el artículo se centrará principalmente en estas cuatro secciones para analizar mejor la cooperación entre China y ALC en el comercio electrónico de la Ruta de la Seda. Este documento tiene la intención de analizar la cooperación en comercio electrónico entre China y ALC en los campos de política, comercio, instalaciones y contacto entre personas y estudiar en qué áreas aún quedan espacios para la cooperación bilateral y cómo ambas partes pueden lograr mejores resultados en los que todos ganan en el marco de la “Iniciativa de la Franja y la Ruta” de China

Predictive Modelling of Quantum Process with Neural Networks

Complete characterization of an unknown quantum process can be achieved by process tomography, or, for continuous time processes, by Hamiltonian learning. However, such a characterization becomes unfeasible for high dimensional quantum systems. In this paper, we develop the first neural network algorithm for predicting the behavior of an unknown quantum process when applied on a given ensemble of input states. The network is trained with classical data obtained from measurements on a few pairs of input/output quantum states. After training, it can be used to predict the measurement statistics of a set of measurements of interest performed on the output state corresponding to any input in the state ensemble. Besides learning a quantum gate or quantum circuit, our model can also be applied to the task of learning a noisy quantum evolution and predicting the measurement statistics on a time-evolving quantum state. We show numerical results using our neural network model for various relevant processes in quantum computing, quantum many-body physics, and quantum optics.Comment: 12 pages, 7 figure

Exact algorithms to minimize interference in wireless sensor networks

AbstractFinding a low-interference connected topology is a fundamental problem in wireless sensor networks (WSNs). The problem of reducing interference through adjusting the nodes’ transmission radii in a connected network is one of the most well-known open algorithmic problems in wireless sensor network optimization. In this paper, we study minimization of the average interference and the maximum interference for the highway model, where all the nodes are arbitrarily distributed on a line. First, we prove that there is always an optimal topology with minimum interference that is planar. Then, two exact algorithms are proposed. The first one is an exact algorithm to minimize the average interference in polynomial time, O(n3Δ), where n is the number of nodes and Δ is the maximum node degree. The second one is an exact algorithm to minimize the maximum interference in sub-exponential time, O(n3ΔO(k)), where k=O(Δ) is the minimum maximum interference. All the optimal topologies constructed are planar

Gabor-based audiovisual fusion for Mandarin Chinese speech recognition

Audiovisual Speech Recognition (AVSR) is a popular research topic, and incorporating visual features into speech recognition systems has been found to deliver good results. In recent years, end-to-end Convolutional Neural Network (CNN) based deep learning has been widely utilized. However, these often require big data and can be time consuming to train. A lot of speech research also tends to focus on English language datasets. In this paper, we propose a lightweight optimized and automated speech recognition system using Gabor based feature extraction, combined with our Audiovisual Mandarin Chinese (AVMC) corpus. This combines Mel-frequency Cepstral Coefficients (MFCCs) + CNN_Bidirectional Long Short-term Memory (BiLSTM)_Attention (CLA) model for Audio Speech Recognition, and low dimension Gabor visual features + CLA model for Visual Speech Recognition. As we are focusing on Chinese language recognition, we individually analyse initials, finals, and tones, as part of pinyin speech production. The proposed low dimensionality system achieves 88.6%, 87.5% and 93.6% accuracy for tones, initials and finals respectively at char-level, 84.8% for pinyin at word-level

A Practical Neighbor Discovery Framework for Wireless Sensor Networks

Neighbor discovery is a crucial operation frequently executed throughout the life cycle of a Wireless Sensor Network (WSN). Various protocols have been proposed to minimize the discovery latency or to prolong the lifetime of sensors. However, none of them have addressed that all the critical concerns stemming from real WSNs, including communication collisions, latency constraints and energy consumption limitations. In this paper, we propose Spear, the first practical neighbor discovery framework to meet all these requirements. Spear offers two new methods to reduce communication collisions, thus boosting the discovery rate of existing neighbor discovery protocols. Spear also takes into consideration latency constraints and facilitate

The Advances in Epigenetics for Cancer Radiotherapy

Cancer is an important factor threatening human life and health; in recent years, its morbidity and mortality remain high and demosntrate an upward trend. It is of great significance to study its pathogenesis and targeted therapy. As the complex mechanisms of epigenetic modification has been increasingly discovered, they are more closely related to the occurrence and development of cancer. As a reversible response, epigenetic modification is of great significance for the improvement of classical therapeutic measures and the discovery of new therapeutic targets. It has become a research focusto explore the multi-level mechanisms of RNA, DNA, chromatin and proteins. As an important means of cancer treatment, radiotherapy has made great progress in technology, methods, means and targeted sensitization after years of rapid development, and even research on radiotherapy based on epigenetic modification is rampant. A series of epigenetic effects of radiation on DNA methylation, histone modification, chromosome remodeling, RNA modification and non-coding RNA during radiotherapy affects the therapeutic effects and prognosis. Starting from the epigenetic mechanism of tumorigenesis, this paper reviews the latest progress in the mechanism of interaction between epigenetic modification and cancer radiotherapy and briefly introduces the main types, mechanisms and applications of epigenetic modifiers used for radiotherapy sensitization in order to explore a more individual and dynamic approach of cancer treatment based on epigenetic mechanism. This study strives to make a modest contribution to the progress of human disease research