arXiv subjects

Rui Li

Publications and source records attributed to Rui Li.

At least 433 records · Page 24Linked to original sources

arXiv2021

Adaptive Processor Frequency Adjustment for Mobile Edge Computing with Intermittent Energy Supply

With astonishing speed, bandwidth, and scale, Mobile Edge Computing (MEC) has played an increasingly important role in the next generation of connectivity and service delivery. Yet, along with the massive deployment of MEC servers, the ensuing energy issue is now on an increasingly urgent agenda. In the current context, the large scale deployment of renewable-energy-supplied MEC servers is perhaps the most promising solution for the incoming energy issue. Nonetheless, as a result of the intermittent nature of their power sources, these special design MEC server must be more cautious about their energy usage, in a bid to maintain their service sustainability as well as service standard. Targeting optimization on a single-server MEC scenario, we in this paper propose NAFA, an adaptive processor frequency adjustment solution, to enable an effective plan of the server's energy usage. By learning from the historical data revealing request arrival and energy harvest pattern, the deep reinforcement learning-based solution is capable of making intelligent schedules on the server's processor frequency, so as to strike a good balance between service sustainability and service quality. The superior performance of NAFA is substantiated by real-data-based experiments, wherein NAFA demonstrates up to 20% increase in average request acceptance ratio and up to 50% reduction in average request processing time.

eess.SY↗

arXiv2021

Inferring Facing Direction from Voice Signals

Consider a home or office where multiple devices are running voice assistants (e.g., TVs, lights, ovens, refrigerators, etc.). A human user turns to a particular device and gives a voice command, such as ``Alexa, can you ...''. This paper focuses on the problem of detecting which device the user was facing, and therefore, enabling only that device to respond to the command. Our core intuition emerges from the fact that human voice exhibits a directional radiation pattern, and the orientation of this pattern should influence the signal received at each device. Unfortunately, indoor multipath, unknown user location, and unknown voice signals pose as critical hurdles. Through a new algorithm that estimates the line-of-sight (LoS) power from a given signal, and combined with beamforming and triangulation, we design a functional solution called CoDIR. Results from $500+$ configurations, across $5$ rooms and $9$ different users, are encouraging. While improvements are necessary, we believe this is an important step forward in a challenging but urgent problem space.

cs.SD↗

arXiv2021

A promotion for odd symmetric discrete Fourier transform

DFT is the numerical implementation of Fourier transform (FT), and it has many forms. Ordinary DFT (ODFT) and symmetric DFT (SDFT) are the two main forms of DFT. The most widely used DFT is ODFT, and the phase spectrum of this form is widely used in engineering applications. However, it is found ODFT has the problem of phase aliasing. Moreover, ODFT does not have many FT properties, such as symmetry, integration, and interpolation. When compared with ODFT, SDFT has more FT properties. Theoretically, the more properties a transformation has, the wider its application range. Hence, SDFT is more suitable as the discrete form of FT. In order to promote SDFT, the unique nature of SDFT is demonstrated. The time-domain of even-point SDFT is not symmetric to zero, and the author corrects it in this study. The author raises a new issue that should the signal length be odd or even when performing SDFT. The answer is odd. However, scientists and engineers are accustomed to using even-numbered sequences. At the end of this study, the reasons why the author advocates odd SDFT are given. Besides, even sampling function, discrete frequency Fourier transform, and the Gibbs phenomenon of the SDFT are introduced.

cs.IT↗

arXiv2021

Eliminating the Barriers: Demystifying Wi-Fi Baseband Design and Introducing the PicoScenes Wi-Fi Sensing Platform

The research on Wi-Fi sensing has been thriving over the past decade but the process has not been smooth. Three barriers always hamper the research: unknown baseband design and its influence, inadequate hardware, and the lack of versatile and flexible measurement software. This paper tries to eliminate these barriers through the following work. First, we present an in-depth study of the baseband design of the Qualcomm Atheros AR9300 (QCA9300) NIC. We identify a missing item of the existing CSI model, namely, the CSI distortion, and identify the baseband filter as its origin. We also propose a distortion removal method. Second, we reintroduce both the QCA9300 and software-defined radio (SDR) as powerful hardware for research. For the QCA9300, we unlock the arbitrary tuning of both the carrier frequency and bandwidth. For SDR, we develop a high?performance software implementation of the 802.11a/g/n/ac/ax baseband, allowing users to fully control the baseband and access the complete physical-layer information. Third, we release the PicoScenes software, which supports concurrent CSI measure?ment from multiple QCA9300, Intel Wireless Link (IWL5300) and SDR hardware. PicoScenes features rich low-level controls, packet injection and software baseband implementation. It also allows users to develop their own measurement plugins. Finally, we report state-of-the-art results in the extensive evaluations of the PicoScenes system, such as the >2 GHz available spectrum on the QCA9300, concurrent CSI measurement, and up to 40 kHz and 1 kHz CSI measurement rates achieved by the QCA9300 and SDR. PicoScenes is available at https://ps.zpj.io.

cs.AR↗

arXiv2021

DP-4-coloring of planar graphs with some restrictions on cycles

DP-coloring was introduced by Dvořák and Postle as a generalization of list coloring. It was originally used to solve a longstanding conjecture by Borodin, stating that every planar graph without cycles of lengths 4 to 8 is 3-choosable. In this paper, we give three sufficient conditions for a planar graph to be DP-4-colorable. Actually all the results (Theorem 1.3, 1.4 and 1.7) are stated in the ``color extendability'' form, and uniformly proved by vertex identification and discharging method.

math.CO↗

arXiv2021

Quotients of probabilistic Boolean networks

A probabilistic Boolean network (PBN) is a discrete-time system composed of a collection of Boolean networks between which the PBN switches in a stochastic manner. This paper focuses on the study of quotients of PBNs. Given a PBN and an equivalence relation on its state set, we consider a probabilistic transition system that is generated by the PBN; the resulting quotient transition system then automatically captures the quotient behavior of this PBN. We therefore describe a method for obtaining a probabilistic Boolean system that generates the transitions of the quotient transition system. Applications of this quotient description are discussed, and it is shown that for PBNs, controller synthesis can be performed easily by first controlling a quotient system and then lifting the control law back to the original network. A biological example is given to show the usefulness of the developed results.

math.OC↗

arXiv2021

Ce-site dilution in the ferromagnetic Kondo lattice CeRh$_6$Ge$_4$

The heavy fermion ferromagnet CeRh$_6$Ge$_4$ is the first example of a clean stoichiometric system where the ferromagnetic transition can be continuously suppressed by hydrostatic pressure to a quantum critical point. In order to reveal the outcome when the magnetic lattice of CeRh$_6$Ge$_4$ is diluted with non-magnetic atoms, this study reports comprehensive measurements of the physical properties of both single crystal and polycrystalline samples of La$_x$Ce$_{1-x}$Rh$_6$Ge$_4$. With increasing $x$, the Curie temperature decreases, and no transition is observed for $x$ $>$ 0.25, while the system evolves from exhibiting coherent Kondo lattice behaviors at low $x$, to the Kondo impurity scenario at large $x$. Moreover, non-Fermi liquid behavior (NFL) is observed over a wide doping range, which agrees well with the disordered Kondo model for 0.52 $\leq$ $x$ $\leq$ 0.66, while strange metal behavior is revealed in the vicinity of $x_c$ = 0.26.

cond-mat.str-el↗

arXiv2021

NMR determination of Van Hove singularity and Lifshitz transitions in nodal-line semimetal ZrSiTe

We have applied nuclear magnetic resonance spectroscopy to study the distinctive network of nodal lines in the Dirac semimetal ZrSiTe. The low-$T$ behavior is dominated by a symmetry-protected nodal line, with NMR providing a sensitive probe of the diamagnetic response of the associated carriers. A sharp low-$T$ minimum in NMR shift and $(T_1T)^{-1}$ provides a quantitative measure of the dispersionless, quasi-2D behavior of this nodal line. We also identify a van Hove singularity closely connected to this nodal line, and an associated $T$-induced Lifshitz transition. A disconnect in the NMR shift and line width at this temperature indicates the change in electronic behavior associated with this topological change. These features have an orientation-dependent behavior indicating a field-dependent scaling of the associated band energies.

cond-mat.str-el↗

arXiv2021

Accurate position tracking with a single UWB anchor

Accurate localization and tracking are a fundamental requirement for robotic applications. Localization systems like GPS, optical tracking, simultaneous localization and mapping (SLAM) are used for daily life activities, research, and commercial applications. Ultra-wideband (UWB) technology provides another venue to accurately locate devices both indoors and outdoors. In this paper, we study a localization solution with a single UWB anchor, instead of the traditional multi-anchor setup. Besides the challenge of a single UWB ranging source, the only other sensor we require is a low-cost 9 DoF inertial measurement unit (IMU). Under such a configuration, we propose continuous monitoring of UWB range changes to estimate the robot speed when moving on a line. Combining speed estimation with orientation estimation from the IMU sensor, the system becomes temporally observable. We use an Extended Kalman Filter (EKF) to estimate the pose of a robot. With our solution, we can effectively correct the accumulated error and maintain accurate tracking of a moving robot.

cs.RO↗

arXiv2021

A Precise Method to Determine the Energy Scale and Resolution using Gamma Calibration Sources in a Liquid Scintillator Detector

Gamma sources are routinely used to calibrate the energy scale and resolution of liquid scintillator detectors. However, non-scintillating material surrounding the source introduces energy losses, which may bias the determination of the centroid and width of the full absorption peak. In this paper, we present a general method to determine the true gamma centroid and width to a relative precision of 0.03\% and 0.50\%, respectively, using energy losses predicted by the Monte Carlo simulation. In particular, the accuracy of the assumed source geometry is readily obtained from the fit. The method performs well with experimental data in the Daya Bay detector.

physics.ins-det↗

arXiv2021

Efficient distributed algorithms for Convolutional Neural Networks

Several efficient distributed algorithms have been developed for matrix-matrix multiplication: the 3D algorithm, the 2D SUMMA algorithm, and the 2.5D algorithm. Each of these algorithms was independently conceived and they trade-off memory needed per node and the inter-node data communication volume. The convolutional neural network (CNN) computation may be viewed as a generalization of matrix-multiplication combined with neighborhood stencil computations. We develop communication-efficient distributed-memory algorithms for CNNs that are analogous to the 2D/2.5D/3D algorithms for matrix-matrix multiplication.

cs.DC↗

arXiv2021

Lens-free Optical Detection of Thermal Motion of a Sub-millimeter Sphere Diamagnetically Levitated in High Vacuum

Levitated oscillators with millimeter or sub-millimeter size are particularly attractive due to their potential role in studying various fundamental problems and practical applications. One of the crucial issues towards these goals is to achieve efficient measurements of oscillator motion, while this remains a challenge. Here we theoretically propose a lens-free optical detection scheme, which can be used to detect the motion of a millimeter or sub-millimeter levitated oscillator with a measurement efficiency close to the standard quantum limit with a modest optical power. We demonstrate experimentally this scheme on a 0.5 mm diameter micro-sphere that is diamagnetically levitated under high vacuum and room temperature, and the thermal motion is detected with high precision. Based on this system, an estimated acceleration sensitivity of $9.7 \times 10^{-10}\rm g/\sqrt{Hz}$ is achieved, which is more than one order improvement over the best value reported by the levitated mechanical system. Due to the stability of the system, the minimum resolved acceleration of $3.5\times 10^{-12}\rm g$ is reached with measurement times of $10^5$ s. This result is expected to have potential applications in the study of exotic interactions in the millimeter or sub-millimeter range and the realization of compact gravimeter and accelerometer.

quant-ph↗

arXiv2021

Searching Collaborative Agents for Multi-plane Localization in 3D Ultrasound

3D ultrasound (US) has become prevalent due to its rich spatial and diagnostic information not contained in 2D US. Moreover, 3D US can contain multiple standard planes (SPs) in one shot. Thus, automatically localizing SPs in 3D US has the potential to improve user-independence and scanning-efficiency. However, manual SP localization in 3D US is challenging because of the low image quality, huge search space and large anatomical variability. In this work, we propose a novel multi-agent reinforcement learning (MARL) framework to simultaneously localize multiple SPs in 3D US. Our contribution is four-fold. First, our proposed method is general and it can accurately localize multiple SPs in different challenging US datasets. Second, we equip the MARL system with a recurrent neural network (RNN) based collaborative module, which can strengthen the communication among agents and learn the spatial relationship among planes effectively. Third, we explore to adopt the neural architecture search (NAS) to automatically design the network architecture of both the agents and the collaborative module. Last, we believe we are the first to realize automatic SP localization in pelvic US volumes, and note that our approach can handle both normal and abnormal uterus cases. Extensively validated on two challenging datasets of the uterus and fetal brain, our proposed method achieves the average localization accuracy of 7.03 degrees/1.59mm and 9.75 degrees/1.19mm. Experimental results show that our light-weight MARL model has higher accuracy than state-of-the-art methods.

cs.CV↗

arXiv2021

TarGAN: Target-Aware Generative Adversarial Networks for Multi-modality Medical Image Translation

Paired multi-modality medical images, can provide complementary information to help physicians make more reasonable decisions than single modality medical images. But they are difficult to generate due to multiple factors in practice (e.g., time, cost, radiation dose). To address these problems, multi-modality medical image translation has aroused increasing research interest recently. However, the existing works mainly focus on translation effect of a whole image instead of a critical target area or Region of Interest (ROI), e.g., organ and so on. This leads to poor-quality translation of the localized target area which becomes blurry, deformed or even with extra unreasonable textures. In this paper, we propose a novel target-aware generative adversarial network called TarGAN, which is a generic multi-modality medical image translation model capable of (1) learning multi-modality medical image translation without relying on paired data, (2) enhancing quality of target area generation with the help of target area labels. The generator of TarGAN jointly learns mapping at two levels simultaneously - whole image translation mapping and target area translation mapping. These two mappings are interrelated through a proposed crossing loss. The experiments on both quantitative measures and qualitative evaluations demonstrate that TarGAN outperforms the state-of-the-art methods in all cases. Subsequent segmentation task is conducted to demonstrate effectiveness of synthetic images generated by TarGAN in a real-world application. Our code is available at https://github.com/2165998/TarGAN.

eess.IV↗

arXiv2021

Learn Fine-grained Adaptive Loss for Multiple Anatomical Landmark Detection in Medical Images

Automatic and accurate detection of anatomical landmarks is an essential operation in medical image analysis with a multitude of applications. Recent deep learning methods have improved results by directly encoding the appearance of the captured anatomy with the likelihood maps (i.e., heatmaps). However, most current solutions overlook another essence of heatmap regression, the objective metric for regressing target heatmaps and rely on hand-crafted heuristics to set the target precision, thus being usually cumbersome and task-specific. In this paper, we propose a novel learning-to-learn framework for landmark detection to optimize the neural network and the target precision simultaneously. The pivot of this work is to leverage the reinforcement learning (RL) framework to search objective metrics for regressing multiple heatmaps dynamically during the training process, thus avoiding setting problem-specific target precision. We also introduce an early-stop strategy for active termination of the RL agent's interaction that adapts the optimal precision for separate targets considering exploration-exploitation tradeoffs. This approach shows better stability in training and improved localization accuracy in inference. Extensive experimental results on two different applications of landmark localization: 1) our in-house prenatal ultrasound (US) dataset and 2) the publicly available dataset of cephalometric X-Ray landmark detection, demonstrate the effectiveness of our proposed method. Our proposed framework is general and shows the potential to improve the efficiency of anatomical landmark detection.

cs.CV↗

arXiv2021

Tuna: A Static Analysis Approach to Optimizing Deep Neural Networks

We introduce Tuna, a static analysis approach to optimizing deep neural network programs. The optimization of tensor operations such as convolutions and matrix multiplications is the key to improving the performance of deep neural networks. Many deep learning model optimization mechanisms today use dynamic analysis, which relies on experimental execution on a target device to build a data-driven cost model of the program. The reliance on dynamic profiling not only requires access to target hardware at compilation time but also incurs significant cost in machine resources. We introduce an approach that profiles the program by constructing features based on the target hardware characteristics in order. We use static analysis of the relative performance of tensor operations to optimize the deep learning program. Experiments show that our approach can achieve up to 11x performance compared to dynamic profiling based methods with the same compilation time.

cs.DC↗

arXiv2021

The Paradigm of Digital Twin Communications

With the fast evolving of cloud computing and artificial intelligence (AI), the concept of digital twin (DT) has recently been proposed and finds broad applications in industrial Internet, IoT, smart city, etc. The DT builds a mirror integrated multi-physics of the physical system in the digital space. By doing so, the DT can utilize the rich computing power and AI at the cloud to operate on the mirror physical system, and accordingly provides feedbacks to help the real-world physical system in their practical task completion. The existing literature mainly considers DT as a simulation/emulation approach, whereas the communication framework for DT has not been clearly defined and discussed. In this article, we describe the basic DT communication models and present the open research issues. By combining wireless communications, artificial intelligence (AI) and cloud computing, we show that the DT communication provides a novel framework for futuristic mobile agent systems.

cs.NI↗

arXiv2021

Lenses In VoicE (LIVE): Searching for strong gravitational lenses in the VOICE@VST survey using Convolutional Neural Networks

We present a sample of 16 likely strong gravitational lenses identified in the VST Optical Imaging of the CDFS and ES1 fields (VOICE survey) using Convolutional Neural Networks (CNNs). We train two different CNNs on composite images produced by superimposing simulated gravitational arcs on real Luminous Red Galaxies observed in VOICE. Specifically, the first CNN is trained on single-band images and more easily identifies systems with large Einstein radii, while the second one, trained on composite RGB images, is more accurate in retrieving systems with smaller Einstein radii. We apply both networks to real data from the VOICE survey, taking advantage of the high limiting magnitude (26.1 in the r-band) and low PSF FWHM (0.8" in the r-band) of this deep survey. We analyse $\sim21,200$ images with $mag_r<21.5$, identifying 257 lens candidates. To retrieve a high-confidence sample and to assess the accuracy of our technique, nine of the authors perform a visual inspection. Roughly 75% of the systems are classified as likely lenses by at least one of the authors. Finally, we assemble the LIVE sample (Lenses In VoicE) composed by the 16 systems passing the chosen grading threshold. Three of these candidates show likely lensing features when observed by the Hubble Space Telescope. This work represents a further confirmation of the ability of CNNs to inspect large samples of galaxies searching for gravitational lenses. These algorithms will be crucial to exploit the full scientific potential of forthcoming surveys with the Euclid satellite and the Vera Rubin Observatory

astro-ph.GA↗