arXiv subjects

Jun Zhang

Publications and source records attributed to Jun Zhang.

At least 973 records · Page 54Linked to original sources

arXiv2019

Learning Clustered Representation for Complex Free Energy Landscapes

In this paper we first analyzed the inductive bias underlying the data scattered across complex free energy landscapes (FEL), and exploited it to train deep neural networks which yield reduced and clustered representation for the FEL. Our parametric method, called Information Distilling of Metastability (IDM), is end-to-end differentiable thus scalable to ultra-large dataset. IDM is also a clustering algorithm and is able to cluster the samples in the meantime of reducing the dimensions. Besides, as an unsupervised learning method, IDM differs from many existing dimensionality reduction and clustering methods in that it neither requires a cherry-picked distance metric nor the ground-true number of clusters, and that it can be used to unroll and zoom-in the hierarchical FEL with respect to different timescales. Through multiple experiments, we show that IDM can achieve physically meaningful representations which partition the FEL into well-defined metastable states hence are amenable for downstream tasks such as mechanism analysis and kinetic modeling.

cond-mat.stat-mech↗

arXiv2019

Mobile Edge Intelligence and Computing for the Internet of Vehicles

The Internet of Vehicles (IoV) is an emerging paradigm, driven by recent advancements in vehicular communications and networking. Advances in research can now provide reliable communication links between vehicles, via vehicle-to-vehicle communications, and between vehicles and roadside infrastructures, via vehicle-to-infrastructure communications. Meanwhile, the capability and intelligence of vehicles are being rapidly enhanced, and this will have the potential of supporting a plethora of new exciting applications, which will integrate fully autonomous vehicles, the Internet of Things (IoT), and the environment. These trends will bring about an era of intelligent IoV, which will heavily depend upon communications, computing, and data analytics technologies. To store and process the massive amount of data generated by intelligent IoV, onboard processing and Cloud computing will not be sufficient, due to resource/power constraints and communication overhead/latency, respectively. By deploying storage and computing resources at the wireless network edge, e.g., radio access points, the edge information system (EIS), including edge caching, edge computing, and edge AI, will play a key role in the future intelligent IoV. Such system will provide not only low-latency content delivery and computation services, but also localized data acquisition, aggregation and processing. This article surveys the latest development in EIS for intelligent IoV. Key design issues, methodologies and hardware platforms are introduced. In particular, typical use cases for intelligent vehicles are illustrated, including edge-assisted perception, mapping, and localization. In addition, various open research problems are identified.

cs.NI↗

arXiv2019

Connectivity-Aware UAV Path Planning with Aerial Coverage Maps

Cellular networks are promising to support effective wireless communications for unmanned aerial vehicles (UAVs), which will help to enable various long-range UAV applications. However, these networks are optimized for terrestrial users, and thus do not guarantee seamless aerial coverage. In this paper, we propose to overcome this difficulty by exploiting controllable mobility of UAVs, and investigate connectivity-aware UAV path planning. To explicitly impose communication requirements on UAV path planning, we introduce two new metrics to quantify the cellular connectivity quality of a UAV path. Moreover, aerial coverage maps are used to provide accurate locations of scattered coverage holes in the complicated propagation environment. We formulate the UAV path planning problem as finding the shortest path subject to connectivity constraints. Based on graph search methods, a novel connectivity-aware path planning algorithm with low complexity is proposed. The effectiveness and superiority of our proposed algorithm are demonstrated using the aerial coverage map of an urban section in Virginia, which is built by ray tracing. Simulation results also illustrate a tradeoff between the path length and connectivity quality of UAVs.

cs.NI↗

arXiv2019

Towards a Model-Independent Measurement of the Halo Mass Function with Observables

In the CDM paradigm, the halo mass function is a sensitive probe of the cosmic structure. In observations, halo mass is typically estimated from its relation with other observables. The resulting halo mass function is subject to systematic bias, such as the Eddington bias, due to the scatter or uncertainty in the observable - mass relation. Exact correction for the bias is not easy, as predictions for the observables are typically model-dependent in simulations. In this paper, we point out an interesting feature in the halo mass function of the concordence $Λ$CDM model: the total halo mass within each evenly-spaced logarithmic mass bin is approximately the same over a large mass range. We show that this property allows us to construct an almost bias-free halo mass function using only an observable (as a halo mass estimator) and stacked weak lensing measurements as long as the scatter between the true halo mass and the observable-inferred mass has a stable form in logarithmic units. The method is not sensitive to the form of the mass-observable relation. We test the idea using cosmological simulations, and show that the method performs very well for realistic observables.

astro-ph.CO↗

arXiv2019

Doubling Phase Shifters for Efficient Hybrid Precoder Design in Millimeter-Wave Communication Systems

Hybrid precoding is a cost-effective approach to support directional transmissions for millimeter-wave (mm-wave) communications, but its precoder design is highly complicated. In this paper, we propose a new hybrid precoder implementation, namely the double phase shifter (DPS) implementation, which enables highly tractable hybrid precoder design. Efficient algorithms are then developed for two popular hybrid precoder structures, i.e., the fully- and partially-connected structures. For the fully-connected one, the RF-only precoding and hybrid precoding problems are formulated as a least absolute shrinkage and selection operator (LASSO) problem and a low-rank matrix approximation problem, respectively. In this way, computationally efficient algorithms are provided to approach the performance of the fully digital one with a small number of radio frequency (RF) chains. On the other hand, the hybrid precoder design in the partially-connected structure is identified as an eigenvalue problem. To enhance the performance of this cost-effective structure, dynamic mapping from RF chains to antennas is further proposed, for which a greedy algorithm and a modified K-means algorithm are developed. Simulation results demonstrate the performance gains of the proposed hybrid precoding algorithms over existing ones. It shows that, with the proposed DPS implementation, the fully-connected structure enjoys both satisfactory performance and low design complexity while the partially-connected one serves as an economic solution with low hardware complexity.

eess.SP↗

arXiv2019

Experimental 4-intensity decoy-state quantum key distribution with asymmetric basis detector efficiency

The decoy-state method has been developed rapidly in quantum key distribution (QKD) since it is immune to photon-number splitting attacks. However, two basis detector efficiency asymmetry, which exists in realistic scenarios, has been ignored in the prior results. By using the recent 4-intensity decoy-state optimization protocol, we report the first implementation of high-rate QKD with asymmetric basis detector efficiency, demonstrating 1.9 to 33.2 times higher key rate than previous protocols in the situation of large basis detector efficiency asymmetry. The results ruled out an implicitly assumption in QKD that the efficiency of Z basis and X basis are restricted to be same. This work paves the way towards a more practical QKD setting.

quant-ph↗

arXiv2019

Effects of finite spatial extent on Schwinger pair production

Electron-positron pair production from vacuum in external electric fields with space and time dependencies is studied numerically using real time Dirac-Heisenberg-Wigner formalism. The influence of spatial focusing scale of the electric field on momentum distribution and the total yield of the particles is examined by considering standing wave mode of the electric field with different temporal configurations. With the decrease of spatial extent of the external field, signatures of the temporal field are weaken in the momentum spectrum. Moreover, in the extremely small spatial extent, novel features emerge due to the combined effects of both temporal and spatial variations. We also find that for dynamically assisted particle production, while the total particle yield drops significantly in small spatial extents, the assistance mechanism tends to increase in these highly inhomogeneous regimes, where the slow and fast pulses are affected differently by the overall spatial inhomogeneity.

hep-ph↗

arXiv2019

LORM: Learning to Optimize for Resource Management in Wireless Networks with Few Training Samples

Effective resource management plays a pivotal role in wireless networks, which, unfortunately, results in challenging mixed-integer nonlinear programming (MINLP) problems in most cases. Machine learning-based methods have recently emerged as a disruptive way to obtain near-optimal performance for MINLPs with affordable computational complexity. There have been some attempts in applying such methods to resource management in wireless networks, but these attempts require huge amounts of training samples and lack the capability to handle constrained problems. Furthermore, they suffer from severe performance deterioration when the network parameters change, which commonly happens and is referred to as the task mismatch problem. In this paper, to reduce the sample complexity and address the feasibility issue, we propose a framework of Learning to Optimize for Resource Management (LORM). Instead of the end-to-end learning approach adopted in previous studies, LORM learns the optimal pruning policy in the branch-and-bound algorithm for MINLPs via a sample-efficient method, namely, imitation learning. To further address the task mismatch problem, we develop a transfer learning method via self-imitation in LORM, named LORM-TL, which can quickly adapt a pre-trained machine learning model to the new task with only a few additional unlabeled training samples. Numerical simulations will demonstrate that LORM outperforms specialized state-of-the-art algorithms and achieves near-optimal performance, while achieving significant speedup compared with the branch-and-bound algorithm. Moreover, LORM-TL, by relying on a few unlabeled samples, achieves comparable performance with the model trained from scratch with sufficient labeled samples.

eess.SP↗

arXiv2019

Two-step evolution of a rising flux rope resulting in a confined solar flare

Combining the Solar Dynamics Observatory and the New Vacuum Solar Telescope observations, we study a confined flare triggered by a rising flux rope within the trailing sunspots of active region 12733. The flux rope lying above the sheared polarity inversion line can be constructed through magnetic extrapolation but could not be detected in multi-wavelength images at the pre-flare stage. The conspicuous shearing motions between the opposite-polarity fields in the photosphere are considered to be responsible for the flux rope formation. The maximum twist of the flux rope is as high as -1.76, and then the flux rope rises due to the kink instability. Only when the flare starts can the flux rope be observed in high-temperature wavelengths. The differential emission measure results confirm that this flux rope is a high-temperature structure. Associated with the rising flux rope, there appear many post-flare loops and a pair of flare ribbons. When the rising flux rope meets and reconnects with the large-scale overlying field lines, a set of large-scale twisted loops are formed, and two flare ribbons propagating in opposite directions appear on the outskirts of the former ribbons, indicating that the twist of the flux rope is transferred to a much larger system. These results imply that the external reconnection between the rising flux rope and the large-scale overlying loops plays an important role in the confined flare formation.

astro-ph.SR↗

arXiv2019

Deep Nearest Class Mean Model for Incremental Odor Classification

In recent years, more machine learning algorithms have been applied to odor classification. These odor classification algorithms usually assume that the training datasets are static. However, for some odor recognition tasks, new odor classes continually emerge. That is, the odor datasets are dynamically growing while both training samples and number of classes are increasing over time. Motivated by this concern, this paper proposes a Deep Nearest Class Mean (DNCM) model based on the deep learning framework and nearest class mean method. The proposed model not only leverages deep neural network to extract deep features, but is also able to dynamically integrate new classes over time. In our experiments, the DNCM model was initially trained with 10 classes, then 25 new classes are integrated. Experiment results demonstrate that the proposed model is very efficient for incremental odor classification, especially for new classes with only a small number of training examples.

cs.LG↗

arXiv2019

Single-photon computational 3D imaging at 45 km

Long-range active imaging has a variety of applications in remote sensing and target recognition. Single-photon LiDAR (light detection and ranging) offers single-photon sensitivity and picosecond timing resolution, which is desirable for high-precision three-dimensional (3D) imaging over long distances. Despite important progress, further extending the imaging range presents enormous challenges because only weak echo photons return and are mixed with strong noise. Herein, we tackled these challenges by constructing a high-efficiency, low-noise confocal single-photon LiDAR system, and developing a long-range-tailored computational algorithm that provides high photon efficiency and super-resolution in the transverse domain. Using this technique, we experimentally demonstrated active single-photon 3D-imaging at a distance of up to 45 km in an urban environment, with a low return-signal level of $\sim$1 photon per pixel. Our system is feasible for imaging at a few hundreds of kilometers by refining the setup, and thus represents a significant milestone towards rapid, low-power, and high-resolution LiDAR over extra-long ranges.

eess.IV↗

arXiv2019

On the 2-Adic Complexity of the Ding-Helleseth-Martinsen Binary Sequences

We determine the 2-adic complexity of the Ding-Helleseth-Martinsen (DHM) binary sequences by using cyclotomic numbers of order four, "Gauss periods" and "quadratic Gauss sum" on finite field $\mathbb{F}_q$ and valued in $\mathbb{Z}_{2^N-1}$ where $q \equiv 5\pmod 8$ is a prime number and $N=2q$ is the period of the DHM sequences.

cs.IT↗

arXiv2019

Constraining Dark Energy With Stacked Concave Lenses

Low density regions are less affected by the nonlinear structure formation and baryonic physics. They are ideal places for probing the nature of dark energy, a possible explanation for the cosmic acceleration. Unlike void lensing, which requires identifications of individual voids, we study the stacked lensing signals around the low-density-positions (LDP), defined as places that are devoid of foreground bright galaxies in projection. The method allows a direct comparison with numerical results by drawing correspondence between the bright galaxies with halos. It leads to lensing signals that are significant enough for differentiating several dark energy models. In this work, we use the CFHTLenS catalogue to define LDPs, as well as measuring their background lensing signals. We consider several different definitions of the foreground bright galaxies (redshift range \& magnitude cut). Regarding the cosmological model, we run six simulations: the first set of simulations have the same initial conditions, with $\rm{w_{de}=-1,-0.5,-0.8,-1.2}$; the second set of simulations include a slightly different $Λ$CDM model and a w(z) model from \cite{2017NatAs...1..627Z}. The lensing results indicate that the models with $\rm{w_{de}=-0.5,-0.8}$ are not favored, and the other four models all achieve comparable agreement with the data.

astro-ph.CO↗

arXiv2019

Flow instabilities in solar jets in their upstream and downstream regimes

Using the Atmospheric Imaging Assembly 304 A images obtained from the Solar Dynamics Observatory, we study two jets which occurred during the M5.8 class flare on 2017 April 3 and the M5.5 class flare on 2016 July 23, respectively. During the M5.8 class flare, many vortex-like structures occurred in the upstream and downstream regimes of the associated jet. While the jet was ejected upwards to the corona, some dark material at its base flowed through a bright structure with a velocity of 110 km/s. The boundary between the material and the structure changed from smooth to uneven. Later, the jet material at the higher atmosphere started to fall down with velocities of over 200 km/s, and the left boundary of the jet developed into a sawtooth pattern. The vortex-like structures were formed, and the growth rates of two structures were presented. During the M5.5 class flare, we also observed many vortex-like structures in the downstream regime of the jet. At the late stage of the jet, some material at the south boundary of the jet fell back to the solar surface, and vortex-like structures at the boundary grew from ripple-like minim into vortices with diameters of 3.4-5.4 Mm. The growth rates of the vortex-like structures were calculated. We suggest that the vortex-like structures in the upstream regime are the manifestations of Kelvin-Helmholtz instability, and those in the downstream regime are simultaneously driven by Kelvin-Helmholtz instability and Raleigh-Taylor instability.

astro-ph.SR↗

arXiv2019

Testing Shear Recovery with Field Distortion

The tilt, rotation, or offset of each CCD with respect to the focal plane, as well as the distortion of the focal plane itself, cause shape distortions to the observed objects, an effect typically known as field distortion (FD). We point out that FD provides a unique way of quantifying the accuracy of cosmic shear measurement. The idea is to stack the shear estimators from galaxies that share similar FD-induced shape distortions. Given that the latter can be calculated with parameters from astrometric calibrations, the accuracy of the shear estimator can be directly tested on real images. It provides a way to calibrate the multiplicative and additive shear recovery biases within the scientific data itself, without requiring simulations or any external data sets. We use the CFHTLenS images to demonstrate the accuracy of the Fourier_Quad shear recovery method. We highlight some details in our image processing pipeline, including background removal, source identification and deblending, astrometric calibration, star selection for PSF reconstruction, noise reduction, etc.. We show that in the shear ranges of -0.005 < g_1 < 0.005 and -0.008 < g_2 < 0.008, the multiplicative biases are at the level of < 0.04. Slight additive biases on the order of 5E-4 (6 sigma) are identified for sources provided by the official CFHTLenS catalog (not using its shear catalog), but are minor (4 sigma) for source catalog generated by our Fourier_Quad pipeline.

astro-ph.CO↗

arXiv2019

On echo intervals in gravitational wave echo analysis

Gravitational wave echoes, if they exist, could encode important information of new physics from the strong gravity regime. Current echo searches usually assume constant interval echoes (CIEs) a priori, although unequal interval echoes (UIEs) are also possible. Despite of its simplicity, the using of CIE templates need to be properly justified, especially given the high sensitivity of future gravitational wave detectors. In this paper, we assess the necessity of UIE templates in echo searches. By reconstructing injected UIE signals with both CIE and UIE templates, we show that the CIE template may significantly misinterpret the echo signals if the variation of the interval is greater than the statistical errors of the interval, which is further confirmed by a Bayesian analysis on model stelection. We also forecast the constraints on the echo intervals given by future GW detectors such as Advanced LIGO and Einstein Telescope.

gr-qc↗

arXiv2019

Wireless Data Acquisition for Edge Learning: Data-Importance Aware Retransmission

By deploying machine-learning algorithms at the network edge, edge learning can leverage the enormous real-time data generated by billions of mobile devices to train AI models, which enable intelligent mobile applications. In this emerging research area, one key direction is to efficiently utilize radio resources for wireless data acquisition to minimize the latency of executing a learning task at an edge server. Along this direction, we consider the specific problem of retransmission decision in each communication round to ensure both reliability and quantity of those training data for accelerating model convergence. To solve the problem, a new retransmission protocol called data-importance aware automatic-repeat-request (importance ARQ) is proposed. Unlike the classic ARQ focusing merely on reliability, importance ARQ selectively retransmits a data sample based on its uncertainty which helps learning and can be measured using the model under training. Underpinning the proposed protocol is a derived elegant communication-learning relation between two corresponding metrics, i.e., signal-to-noise ratio (SNR) and data uncertainty. This relation facilitates the design of a simple threshold based policy for importance ARQ. The policy is first derived based on the classic classifier model of support vector machine (SVM), where the uncertainty of a data sample is measured by its distance to the decision boundary. The policy is then extended to the more complex model of convolutional neural networks (CNN) where data uncertainty is measured by entropy. Extensive experiments have been conducted for both the SVM and CNN using real datasets with balanced and imbalanced distributions. Experimental results demonstrate that importance ARQ effectively copes with channel fading and noise in wireless data acquisition to achieve faster model convergence than the conventional channel-aware ARQ.

cs.IT↗

arXiv2019

The dynamics of AR 12700 in its early emerging phase I: interchange reconnection

The emergence of active regions (ARs) leads to various dynamic activities. Using high-resolution and long-lasting H$α$ observations from the New Vacuum Solar Telescope, we report the dynamics of NOAA AR 12700 in its emerging phase on 26 February 2018 in detail. In this AR, constant interchange reconnections between emerging fibrils and preexisting ones were detected. Driven by the flux emergence, small-scale fibrils observed in H$α$ wavelength continuously emerged at the center of the AR and reconnected with the ambient preexisting fibrils, forming new longer fibrils. We investigate three scenarios of such interchange reconnection in two hours. Specially, the third scenario of reconnection resulted in the formation of longer fibrils that show pronounced rotation motion. To derive the evolution of the magnetic structure during the reconnections, we perform nonlinear force-free field extrapolations. The extrapolated three-dimensional magnetic fields clearly depict a set of almost potential emerging loops, two preexisting flux ropes at 03:00 UT before the second reconnection scenario, and a set of newly formed loops with less twist at 03:48 UT after the third reconnection scenario. All of these extrapolated structures are consistent with the fibrils detected in H$α$ wavelength. The aforementioned observations and extrapolation results suggest that the constant interchange reconnections resulted in that the magnetic twist was redistributed from preexisting flux ropes towards the newly-formed system with longer magnetic structure and weaker twist.

astro-ph.SR↗