arXiv subjects

Yu Chen

Publications and source records attributed to Yu Chen.

At least 397 records · Page 22Linked to original sources

arXiv2022

A Study of Shared-Control with Force Feedback for Obstacle Avoidance in Whole-body Telelocomotion of a Wheeled Humanoid

Teleoperation has emerged as an alternative solution to fully-autonomous systems for achieving human-level capabilities on humanoids. Specifically, teleoperation with whole-body control is a promising hands-free strategy to command humanoids but demands more physical and mental effort. To mitigate this limitation, researchers have proposed shared-control methods incorporating robot decision-making to aid humans on low-level tasks, further reducing operation effort. However, shared-control methods for wheeled humanoid telelocomotion on a whole-body level has yet to be explored. In this work, we study how whole-body feedback affects the performance of different shared-control methods for obstacle avoidance in diverse environments. A Time-Derivative Sigmoid Function (TDSF) is proposed to generate more intuitive force feedback from obstacles. Comprehensive human experiments were conducted, and the results concluded that force feedback enhances the whole-body telelocomotion performance in unfamiliar environments but could reduce performance in familiar environments. Conveying the robot's intention through haptics showed further improvements since the operator can utilize the force feedback for short-distance planning and visual feedback for long-distance planning.

cs.RO↗

arXiv2022

Spacecraft depth completion based on the gray image and the sparse depth map

Perceiving the three-dimensional (3D) structure of the spacecraft is a prerequisite for successfully executing many on-orbit space missions, and it can provide critical input for many downstream vision algorithms. In this paper, we propose to sense the 3D structure of spacecraft using light detection and ranging sensor (LIDAR) and a monocular camera. To this end, Spacecraft Depth Completion Network (SDCNet) is proposed to recover the dense depth map based on gray image and sparse depth map. Specifically, SDCNet decomposes the object-level spacecraft depth completion task into foreground segmentation subtask and foreground depth completion subtask, which segments the spacecraft region first and then performs depth completion on the segmented foreground area. In this way, the background interference to foreground spacecraft depth completion is effectively avoided. Moreover, an attention-based feature fusion module is also proposed to aggregate the complementary information between different inputs, which deduces the correlation between different features along the channel and the spatial dimension sequentially. Besides, four metrics are also proposed to evaluate object-level depth completion performance, which can more intuitively reflect the quality of spacecraft depth completion results. Finally, a large-scale satellite depth completion dataset is constructed for training and testing spacecraft depth completion algorithms. Empirical experiments on the dataset demonstrate the effectiveness of the proposed SDCNet, which achieves 0.25m mean absolute error of interest and 0.759m mean absolute truncation error, surpassing state-of-the-art methods by a large margin. The spacecraft pose estimation experiment is also conducted based on the depth completion results, and the experimental results indicate that the predicted dense depth map could meet the needs of downstream vision tasks.

cs.CV↗

arXiv2022

Effective Multi-User Delay-Constrained Scheduling with Deep Recurrent Reinforcement Learning

Multi-user delay constrained scheduling is important in many real-world applications including wireless communication, live streaming, and cloud computing. Yet, it poses a critical challenge since the scheduler needs to make real-time decisions to guarantee the delay and resource constraints simultaneously without prior information of system dynamics, which can be time-varying and hard to estimate. Moreover, many practical scenarios suffer from partial observability issues, e.g., due to sensing noise or hidden correlation. To tackle these challenges, we propose a deep reinforcement learning (DRL) algorithm, named Recurrent Softmax Delayed Deep Double Deterministic Policy Gradient ($\mathtt{RSD4}$), which is a data-driven method based on a Partially Observed Markov Decision Process (POMDP) formulation. $\mathtt{RSD4}$ guarantees resource and delay constraints by Lagrangian dual and delay-sensitive queues, respectively. It also efficiently tackles partial observability with a memory mechanism enabled by the recurrent neural network (RNN) and introduces user-level decomposition and node-level merging to ensure scalability. Extensive experiments on simulated/real-world datasets demonstrate that $\mathtt{RSD4}$ is robust to system dynamics and partially observable environments, and achieves superior performances over existing DRL and non-DRL-based methods.

cs.LG↗

arXiv2022

A Compacted Structure for Cross-domain learning on Monocular Depth and Flow Estimation

Accurate motion and depth recovery is important for many robot vision tasks including autonomous driving. Most previous studies have achieved cooperative multi-task interaction via either pre-defined loss functions or cross-domain prediction. This paper presents a multi-task scheme that achieves mutual assistance by means of our Flow to Depth (F2D), Depth to Flow (D2F), and Exponential Moving Average (EMA). F2D and D2F mechanisms enable multi-scale information integration between optical flow and depth domain based on differentiable shallow nets. A dual-head mechanism is used to predict optical flow for rigid and non-rigid motion based on a divide-and-conquer manner, which significantly improves the optical flow estimation performance. Furthermore, to make the prediction more robust and stable, EMA is used for our multi-task training. Experimental results on KITTI datasets show that our multi-task scheme outperforms other multi-task schemes and provide marked improvements on the prediction results.

cs.CV↗

arXiv2022

Influence of Binomial Crossover on Approximation Error of Evolutionary Algorithms

Although differential evolution (DE) algorithms perform well on a large variety of complicated optimization problems, only a few theoretical studies are focused on the working principle of DE algorithms. To make the first attempt to reveal the function of binomial crossover, this paper aims to answer whether it can reduce the approximation error of evolutionary algorithms. By investigating the expected approximation error and the probability of not finding the optimum, we conduct a case study comparing two evolutionary algorithms with and without binomial crossover on two classical benchmark problems: OneMax and Deceptive. It is proven that using binomial crossover leads to the dominance of transition matrices. As a result, the algorithm with binomial crossover asymptotically outperforms that without crossover on both OneMax and Deceptive, and outperforms on OneMax, however, not on Deceptive. Furthermore, an adaptive parameter strategy is proposed which can strengthen the superiority of binomial crossover on Deceptive.

cs.NE↗

arXiv2022

A Secure Dynamic Edge Resource Federation Architecture for Cross-Domain IoT Systems

The fast integration of 5G communication, Artificial Intelligence (AI), and Internet-of-Things (IoT) technologies is envisioned to enable Next Generation Networks (NGNs) for diverse smart services and user-defined applications for Smart Cities. However, it is still challenging to build a scalable and efficient infrastructure that satisfies the various performance, security, and management demands by heterogeneous IoT applications across multiple administrative domains. This paper presents a dynamic edge resource federation architecture, which integrates the concept of network slicing (NS) and blockchain to improve scalability, dynamicity, and security for multi-domain IoT applications. A NS-enabled dynamic edge resource federation framework adopts intelligent mechanisms to support efficient multi-domain service coordination that satisfies diverse Quality of Service (QoS) and security requirements. We propose a Hierarchical Integrated Federated Ledger (HIFL), which aims to guarantee decentralized security and privacy-preserving properties in multi-domain resource orchestration and service re-adjustment. As a secure-by-design solution, HIFL is promising to support efficient, trust and secured end-to-end IoT services. A preliminary proof-of-concept prototype has been implemented for comparing intra- and inter-domain performance expectations.

cs.NI↗

arXiv2022

DeFakePro: Decentralized DeepFake Attacks Detection using ENF Authentication

Advancements in generative models, like Deepfake allows users to imitate a targeted person and manipulate online interactions. It has been recognized that disinformation may cause disturbance in society and ruin the foundation of trust. This article presents DeFakePro, a decentralized consensus mechanism-based Deepfake detection technique in online video conferencing tools. Leveraging Electrical Network Frequency (ENF), an environmental fingerprint embedded in digital media recording, affords a consensus mechanism design called Proof-of-ENF (PoENF) algorithm. The similarity in ENF signal fluctuations is utilized in the PoENF algorithm to authenticate the media broadcasted in conferencing tools. By utilizing the video conferencing setup with malicious participants to broadcast deep fake video recordings to other participants, the DeFakePro system verifies the authenticity of the incoming media in both audio and video channels.

cs.CR↗

arXiv2022

Real-Time Elderly Monitoring for Senior Safety by Lightweight Human Action Recognition

With an increasing number of elders living alone, care-giving from a distance becomes a compelling need, particularly for safety. Real-time monitoring and action recognition are essential to raise an alert timely when abnormal behaviors or unusual activities occur. While wearable sensors are widely recognized as a promising solution, highly depending on user's ability and willingness makes them inefficient. In contrast, video streams collected through non-contact optical cameras provide richer information and release the burden on elders. In this paper, leveraging the Independently-Recurrent neural Network (IndRNN) we propose a novel Real-time Elderly Monitoring for senior Safety (REMS) based on lightweight human action recognition (HAR) technology. Using captured skeleton images, the REMS scheme is able to recognize abnormal behaviors or actions and preserve the user's privacy. To achieve high accuracy, the HAR module is trained and fine-tuned using multiple databases. An extensive experimental study verified that REMS system performs action recognition accurately and timely. REMS meets the design goals as a privacy-preserving elderly safety monitoring system and possesses the potential to be adopted in various smart monitoring systems.

cs.CV↗

arXiv2022

Suppressing quantum errors by scaling a surface code logical qubit

Practical quantum computing will require error rates that are well below what is achievable with physical qubits. Quantum error correction offers a path to algorithmically-relevant error rates by encoding logical qubits within many physical qubits, where increasing the number of physical qubits enhances protection against physical errors. However, introducing more qubits also increases the number of error sources, so the density of errors must be sufficiently low in order for logical performance to improve with increasing code size. Here, we report the measurement of logical qubit performance scaling across multiple code sizes, and demonstrate that our system of superconducting qubits has sufficient performance to overcome the additional errors from increasing qubit number. We find our distance-5 surface code logical qubit modestly outperforms an ensemble of distance-3 logical qubits on average, both in terms of logical error probability over 25 cycles and logical error per cycle ($2.914\%\pm 0.016\%$ compared to $3.028\%\pm 0.023\%$). To investigate damaging, low-probability error sources, we run a distance-25 repetition code and observe a $1.7\times10^{-6}$ logical error per round floor set by a single high-energy event ($1.6\times10^{-7}$ when excluding this event). We are able to accurately model our experiment, and from this model we can extract error budgets that highlight the biggest challenges for future systems. These results mark the first experimental demonstration where quantum error correction begins to improve performance with increasing qubit number, illuminating the path to reaching the logical error rates required for computation.

quant-ph↗

arXiv2022

Afterpulse measurement of JUNO 20-inch PMTs

In this article we present the large photo-multiplier tube (PMT) afterpulse measurement results of Jiangmen Underground Neutrino Observatory (JUNO) experiment. Totally 11 dynode-PMTs (R12860) from Hamamatsu company and 150 micro-channel plate PMTs (MCP-PMTs, GDB-6201) from NNVT company were tested, an afterpulse model is built according to the afterpulse time distribution and probability of occurrence for these two types of PMTs. The average ratio between the total afterpulse charge with the delay between 0.5 $μ$ s and 20 $μ$ s to the primary pulse charge is 5.6%(13.2%) for the tested MCP-PMTs (dynode-PMTs). JUNO experiment will deploy 20,012 20-inch PMTs, and this study will benefit the detector simulation, event reconstruction and data analysis of JUNO experiment.

physics.ins-det↗

arXiv2022

Incompatibility measures in multi-parameter quantum estimation under hierarchical quantum measurements

The incompatibility of the measurements constraints the achievable precisions in multi-parameter quantum estimation. Understanding the tradeoff induced by such incompatibility is a central topic in quantum metrology. Here we provide an approach to study the incompatibility under general $p$-local measurements, which are the measurements that can be performed collectively on at most $p$ copies of quantum states. We demonstrate the power of the approach by presenting a hierarchy of analytical bounds on the tradeoff among the precision limits of different parameters. These bounds lead to a necessary condition for the saturation of the quantum Cramér-Rao bound under $p$-local measurements, which recovers the partial commutative condition at p=1 and the weak commutative condition at $p=\infty$. As a further demonstration of the power of the framework, we present another set of tradeoff relations with the right logarithmic operators(RLD).

quant-ph↗

arXiv2022

Information geometry under hierarchical quantum measurement

In most quantum technologies, measurements need to be performed on the parametrized quantum states to transform the quantum information to classical information. The measurements, however, inevitably distort the information. The characterization of the discrepancy is an important subject in quantum information science, which plays a key role in understanding the difference between the structures of the quantum and classical information. Here we analyze the discrepancy in terms of the Fisher information metric and present a framework that can provide analytical bounds on the difference under hierarchical quantum measurements. Specifically, we present a set of analytical bounds on the difference between the quantum and classical Fisher information metric under hierarchical p-local quantum measurements, which are measurements that can be performed collectively on at most p copies of quantum states. The results can be directly transformed to the precision limit in multi-parameter quantum metrology, which leads to characterizations of the tradeoff among the precision of different parameters. The framework also provides a coherent picture for various existing results by including them as special cases.

quant-ph↗

arXiv2022

Damping signatures at JUNO, a medium-baseline reactor neutrino oscillation experiment

We study damping signatures at the Jiangmen Underground Neutrino Observatory (JUNO), a medium-baseline reactor neutrino oscillation experiment. These damping signatures are motivated by various new physics models, including quantum decoherence, $ν_3$ decay, neutrino absorption, and wave packet decoherence. The phenomenological effects of these models can be characterized by exponential damping factors at the probability level. We assess how well JUNO can constrain these damping parameters and how to disentangle these different damping signatures at JUNO. Compared to current experimental limits, JUNO can significantly improve the limits on $τ_3/m_3$ in the $ν_3$ decay model, the width of the neutrino wave packet $σ_x$, and the intrinsic relative dispersion of neutrino momentum $σ_{\rm rel}$.

hep-ex↗

arXiv2022

A multiferroic two-dimensional electron gas

Multiferroics are compounds in which at least two ferroic orders coexist - typically (anti)ferromagnetism and ferroelectricity. While magnetic order can arise in both insulating and conducting compounds, ferroelectricity is in principle not allowed in metals although a few two-dimensional (semi)metals were reported to behave as ferroelectrics. Yet, the combination with magnetic order to realize multiferroic metals remains elusive. Here, by combining x-ray spectroscopy and magnetotransport, we show the coexistence of ferroelectricity and magnetism in an oxide-based two-dimensional electron gas (2DEG). The data evidence a non-volatile switching of the polar displacements and of the anomalous Hall effect by the polarization direction, demonstrating a magnetoelectric coupling. Our findings provide new opportunities in quantum matter stemming from the interplay between ferroelectricity, ferromagnetism and Rashba spin-orbit coupling in a 2DEG.

cond-mat.mtrl-sci↗

arXiv2022

A Magnetic Flux Rope Configuration Derived by Optimization of Two-Spacecraft In-situ Measurements

Increasingly one interplanetary coronal mass ejection (ICME) structure can propagate across more than one spacecraft in the solar wind. This usually happens when two or more spacecraft are nearly radially aligned with a relatively small longitudinal separation angle from one another. This provides multi-point measurements of the same structure and enables better characterization and validation of modeling results of the structures embedded in these ICMEs. We report such an event during October 13-14, 2019 when the Solar TErrestrial RElations Observatory Ahead (STA) spacecraft and the Parker Solar Probe (PSP) crossed one ICME structure at two different locations with nominal separations in both heliocentric distances and the longitudinal angles. We first perform an optimal fitting to the STA in-situ measurements, based on an analytic quasi-three dimensional (3D) model, yielding a minimum reduced $χ^2=0.468$. Then we further apply the optimization approach by combining the magnetic field measurements from both spacecraft along their separate paths across the ICME structure. We find that the output based on the optimization (with the minimum reduced $χ^2=3.15$) of the combined two-spacecraft dataset yields a more consistent result, given the much improved agreement of the model output with PSP data. The result demonstrates a magnetic flux rope configuration with clear 3D spatial variations.

astro-ph.SR↗

arXiv2022

Calibration Strategy of the JUNO-TAO Experiment

The Taishan Antineutrino Observatory (JUNO-TAO, or TAO) is a satellite detector for the Jiangmen Underground Neutrino Observatory (JUNO). Located near the Taishan reactor, TAO independently measures the reactor's antineutrino energy spectrum with unprecedented energy resolution. To achieve this goal, energy response must be well calibrated. Using the Automated Calibration Unit (ACU) and the Cable Loop System (CLS) of TAO, multiple radioactive sources are deployed to various positions in the detector to perform a precise calibration of energy response. The non-linear energy response can be controlled within 0.6% with different energy points of these radioactive sources. It can be further improved by using $^{12}\rm B$ decay signals produced by cosmic muons. Through the energy non-uniformity calibration, residual non-uniformity is less than 0.2%. The energy resolution degradation and energy bias caused by the residual non-uniformity can be controlled within 0.05% and 0.3%, respectively. In addition, the stability of other detector parameters, such as the gain of each silicon photo-multiplier, can be monitored with a special ultraviolet LED calibration system.

physics.ins-det↗

arXiv2022

Adversarial attacks and defenses in Speaker Recognition Systems: A survey

Speaker recognition has become very popular in many application scenarios, such as smart homes and smart assistants, due to ease of use for remote control and economic-friendly features. The rapid development of SRSs is inseparable from the advancement of machine learning, especially neural networks. However, previous work has shown that machine learning models are vulnerable to adversarial attacks in the image domain, which inspired researchers to explore adversarial attacks and defenses in Speaker Recognition Systems (SRS). Unfortunately, existing literature lacks a thorough review of this topic. In this paper, we fill this gap by performing a comprehensive survey on adversarial attacks and defenses in SRSs. We first introduce the basics of SRSs and concepts related to adversarial attacks. Then, we propose two sets of criteria to evaluate the performance of attack methods and defense methods in SRSs, respectively. After that, we provide taxonomies of existing attack methods and defense methods, and further review them by employing our proposed criteria. Finally, based on our review, we find some open issues and further specify a number of future directions to motivate the research of SRSs security.

cs.CR↗

arXiv2022

Wide-angle giant photonic spin Hall effect

Photonic spin Hall effect is a manifestation of spin-orbit interaction of light and can be measured by a transverse shift λof photons with opposite spins. The precise measurement of transverse shifts can enable many spin-related applications, such as precise metrology and optical sensing. However, this transverse shift is generally small (i.e. δ/λ<{10}^{-1}, λis the wavelength), which impedes its precise measurement. To-date proposals to generate giant spin Hall effect (namely with δ/λ>{10}^{2}) have severe limitations, particularly its occurrence only over a narrow angular cone (with a width of Δθ<{1}^{\circ}). Here we propose a universal scheme to realize the wide-angle giant photonic spin Hall effect with Δθ>{70}^{\circ} by exploiting the interface between free space and uniaxial epsilon-near-zero media. The underlying mechanism is ascribed to the almost-perfect polarization splitting between s and p polarized waves at the designed interface. Remarkably, this almost-perfect polarization splitting does not resort to the interference effect and is insensitive to the incident angle, which then gives rise to the wide-angle giant photonic spin Hall effect.

physics.optics↗