Quantization Errors of fGn and fBm Signals

In this Letter, we show that under the assumption of high resolution, the quantization errors of fGn and fBm signals with uniform quantizer can be treated as uncorrelated white noises

Personal Privacy Protection Problems in the Digital Age

With the development of Internet technology, the issue of privacy leakage has attracted more and more attention from the public. In our daily life, mobile phone applications and identity documents that we use may bring the risk of privacy leakage, which had increasingly aroused public concern. The path of privacy protection in the digital age remains to be explored. To explore the source of this risk and how it can be reduced, we conducted this study by using personal experience, collecting data and applying the theory.Comment: 9 pages, 3 figure

Unsupervised Massive MIMO Channel Estimation with Dual-Path Knowledge-Aware Auto-Encoders

In this paper, an unsupervised deep learning framework based on dual-path model-driven variational auto-encoders (VAE) is proposed for angle-of-arrivals (AoAs) and channel estimation in massive MIMO systems. Specifically designed for channel estimation, the proposed VAE differs from the original VAE in two aspects. First, the encoder is a dual-path neural network, where one path uses the received signal to estimate the path gains and path angles, and another uses the correlation matrix of the received signal to estimate AoAs. Second, the decoder has fixed weights that implement the signal propagation model, instead of learnable parameters. This knowledge-aware decoder forces the encoder to output meaningful physical parameters of interests (i.e., path gains, path angles, and AoAs), which cannot be achieved by original VAE. Rigorous analysis is carried out to characterize the multiple global optima and local optima of the estimation problem, which motivates the design of the dual-path encoder. By alternating between the estimation of path gains, path angles and the estimation of AoAs, the encoder is proved to converge. To further improve the convergence performance, a low-complexity procedure is proposed to find good initial points. Numerical results validate theoretical analysis and demonstrate the performance improvements of our proposed framework

Terrestrial Locomotion of PogoX: From Hardware Design to Energy Shaping and Step-to-step Dynamics Based Control

We present a novel controller design on a robotic locomotor that combines an aerial vehicle with a spring-loaded leg. The main motivation is to enable the terrestrial locomotion capability on aerial vehicles so that they can carry heavy loads: heavy enough that flying is no longer possible, e.g., when the thrust-to-weight ratio (TWR) is small. The robot is designed with a pogo-stick leg and a quadrotor, and thus it is named as PogoX. We show that with a simple and lightweight spring-loaded leg, the robot is capable of hopping with TWR $<1$. The control of hopping is realized via two components: a vertical height control via control Lyapunov function-based energy shaping, and a step-to-step (S2S) dynamics based horizontal velocity control that is inspired by the hopping of the Spring-Loaded Inverted Pendulum (SLIP). The controller is successfully realized on the physical robot, showing dynamic terrestrial locomotion of PogoX which can hop at variable heights and different horizontal velocities with robustness to ground height variations and external pushes.Comment: 7 pages, 7 figure

SEA: A Scalable Entity Alignment System

Entity alignment (EA) aims to find equivalent entities in different knowledge graphs (KGs). State-of-the-art EA approaches generally use Graph Neural Networks (GNNs) to encode entities. However, most of them train the models and evaluate the results in a fullbatch fashion, which prohibits EA from being scalable on largescale datasets. To enhance the usability of GNN-based EA models in real-world applications, we present SEA, a scalable entity alignment system that enables to (i) train large-scale GNNs for EA, (ii) speed up the normalization and the evaluation process, and (iii) report clear results for users to estimate different models and parameter settings. SEA can be run on a computer with merely one graphic card. Moreover, SEA encompasses six state-of-the-art EA models and provides access for users to quickly establish and evaluate their own models. Thus, SEA allows users to perform EA without being involved in tedious implementations, such as negative sampling and GPU-accelerated evaluation. With SEA, users can gain a clear view of the model performance. In the demonstration, we show that SEA is user-friendly and is of high scalability even on computers with limited computational resources.Comment: SIGIR'23 Demo Trac

LivePhoto: Real Image Animation with Text-guided Motion Control

Despite the recent progress in text-to-video generation, existing studies usually overlook the issue that only spatial contents but not temporal motions in synthesized videos are under the control of text. Towards such a challenge, this work presents a practical system, named LivePhoto, which allows users to animate an image of their interest with text descriptions. We first establish a strong baseline that helps a well-learned text-to-image generator (i.e., Stable Diffusion) take an image as a further input. We then equip the improved generator with a motion module for temporal modeling and propose a carefully designed training pipeline to better link texts and motions. In particular, considering the facts that (1) text can only describe motions roughly (e.g., regardless of the moving speed) and (2) text may include both content and motion descriptions, we introduce a motion intensity estimation module as well as a text re-weighting module to reduce the ambiguity of text-to-motion mapping. Empirical evidence suggests that our approach is capable of well decoding motion-related textual instructions into videos, such as actions, camera movements, or even conjuring new contents from thin air (e.g., pouring water into an empty glass). Interestingly, thanks to the proposed intensity learning mechanism, our system offers users an additional control signal (i.e., the motion intensity) besides text for video customization.Comment: Project page: https://xavierchen34.github.io/LivePhoto-Page

E-waste polycyclic aromatic hydrocarbon (PAH) exposure leads to child gut-mucosal inflammation and adaptive immune response

Polycyclic aromatic hydrocarbon (PAH) exposure alters immunological responses. Research concerning PAH exposure on intestinal immunity of children in electronic waste (e-waste) areas is scarce. The aim of this study was to evaluate the effects of polycyclic aromatic hydrocarbon (PAH) pollutants on intestinal mucosal immunity of children in e-waste areas. Results showed higher hydroxylated PAH (OH-PAH) concentrations in e-waste-exposed children, accompanied with higher sialyl Lewis A (SLA) level, absolute lymphocyte and monocyte counts, decreased of percentage of CD4(+) T cells, and had a higher risk of diarrhea. OH-PAH concentrations were negative with child growth. 1-OHNap mediated through WBCs, along with 1-OHPyr, was correlated with an increase SLA concentration. 2-OHFlu, 1-OHPhe, 2-OHPhe, 1-OHPyr, and 6-OHChr were positively correlated with secretory immunoglobulin A (sIgA) concentration. Our results indicated that PAH pollutants caused inflammation, affected the intestinal epithelium, and led to transformation of microfold cell (M cell). M cells initiating mucosal immune responses and the subsequent increasing sIgA production might be an adaptive immune respond of children in the e-waste areas. To our knowledge, this is the first study of PAH exposure on children intestinal immunity in e-waste area, showing that PAH exposure plays a negative role in child growth and impairs the intestinal immune function

Relations of blood lead levels to echocardiographic left ventricular structure and function in preschool children

Lead (Pb) has been proved to exert adverse effect on human cardiovascular system. However, the cardiotoxicity of Pb on children is still unclear. The aim of this study was to evaluate left ventricular (LV) structure and function, by using echocardiographic indices, in order to elucidate the effect of Pb on low-grade inflammation related to left ventricle in healthy preschool children. We recruited a total of 486 preschool children, 310 from Guiyu (e-waste-exposed area) and 176 from Haojiang (reference area). Blood Pb levels, complete blood counts, and LV parameters were evaluated. Associations between blood Pb levels and LV parameters and peripheral leukocyte counts were analyzed using linear regression models. The median blood level of Pb and the counts of white blood cells (WBCs), monocytes, and neutrophils were higher in exposed group. In addition, the exposed group showed smaller left ventricle (including interventricular septum, LV posterior wall, and LV mass index) and impaired LV systolic function (including LV fractional shortening and LV ejection fraction) regardless gender. After adjustment for confounding factors, elevated blood Pb levels were significantly associated with higher counts of WBCs and neutrophils, and lower levels of LV parameters. Furthermore, counts of WBCs, monocytes, and neutrophils were negatively correlated with LV parameters. Taken together, smaller left ventricle and impaired systolic function were found in e-waste-exposed children and associated with chronic low-grade inflammation and elevated blood Pb levels. It indicates that the heart health of e-waste-exposed children is at risk due to the long-term environmental chemical insults. (C) 2020 Elsevier Ltd. All rights reserved