arXiv subjects

Ke Zhang

Publications and source records attributed to Ke Zhang.

At least 307 records · Page 17Linked to original sources

arXiv2020

Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that such deployments can also be used to enable advanced data-driven and Machine Learning (ML) applications in mobile networks. We propose an edge-controller-based architecture for cellular networks and evaluate its performance with real data from hundreds of base stations of a major U.S. operator. In this regard, we will provide insights on how to dynamically cluster and associate base stations and controllers, according to the global mobility patterns of the users. Then, we will describe how the controllers can be used to run ML algorithms to predict the number of users in each base station, and a use case in which these predictions are exploited by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that the prediction accuracy improves when based on machine learning algorithms that rely on the controllers' view and, consequently, on the spatial correlation introduced by the user mobility, with respect to when the prediction is based only on the local data of each single base station.

cs.NI↗

arXiv2020

Hints of a Population of Solar System Analog Planets from ALMA

The recent ALMA DSHARP survey provided illuminating results on the diversity of substructures in planet forming disks. These substructures trace pebble-sized grains accumulated at local pressure maxima, possibly due to planet-disk interactions or other planet formation processes. DSHARP sources are heavily biased to large and massive disks that only represent the high (dust flux) tail end of the disk population. Thus it is unclear whether similar substructures and corresponding physical processes also occur in the majority of disks which are fainter and more compact. Here we explore the presence and characteristics of features in a compact disk around GQ Lup A, the effective radius of which is 1.5 to 10 times smaller than those of DSHARP disks. We present our analysis of ALMA 1.3mm continuum observations of the GQ Lup system. By fitting visibility profiles of the continuum emission, we find substructures including a gap at ~ 10 au. The compact disk around GQ Lup exhibits similar substructures to those in the DSHARP sample, suggesting that mechanisms of trapping pebble-sized grains are at work in small disks as well. Characteristics of the feature at ~ 10 au, if due to a hidden planet, are evidence of planet formation at Saturnian distances. Our results hint at a rich world of substructures to be identified within the common population of compact disks, and subsequently a population of solar system analogs within these disks. Such study is critical to understanding the formation mechanisms and planet populations in the majority of protoplanetary disks.

astro-ph.SR↗

arXiv2020

Detection of missing low-lying atomic states in actinium

Two lowest-energy odd-parity atomic levels of actinium, 7s^27p 2P^o_1/2, 7s^27p 2P^o_3/2, were observed via two-step resonant laser-ionization spectroscopy and their respective energies were measured to be 7477.36(4) cm^-1 and 12 276.59(2) cm^-1. The lifetimes of these states were determined as 668(11) ns and 255(7) ns, respectively. In addition, these properties were calculated using a hybrid approach that combines configuration interaction and coupled-cluster methods in good agreement. The data are of relevance for understanding the complex atomic spectra of actinides and for developing efficient laser-cooling and ionization schemes for actinium, with possible applications for high-purity medicalisotope production and future fundamental physics experiments with this atom.

physics.atom-ph↗

arXiv2020

Spectroscopic realization of large surface gap in a doped magnetic topological insulator

Realization of the quantum anomalous Hall effect and axion electrodynamics in topological materials are among the paradigmatic phenomena in condensed matter physics. Recently, signatures of both phases are observed to exist in thin films of MnBi$_2$Te$_4$, a stoichiometric antiferromagnetic topological insulator. Direct evidence of the bulk topological magnetoelectric response in an axion insulator requires an energy gap at its topological surface state (TSS). However, independent spectroscopic experiments revealed that such a surface gap is absent, or much smaller than previously thought, in MnBi$_2$Te$_4$. Here, we utilize angle resolved photoemission spectroscopy and density functional theory calculations to demonstrate that a sizable TSS gap unexpectedly exists in Sb-doped MnBi$_2$Te$_4$. This gap is found to be topologically nontrivial, insensitive to the bulk antiferromagnetic-paramagnetic transition, while enlarges along with increasing Sb concentration. Our work shows that Mn(Bi$_{1-x}$Sb$_x$)$_2$Te$_4$ is a potential platform to observe the key features of the high-temperature axion insulator state, such as the topological magnetoelectric responses and half-integer quantum Hall effects.

cond-mat.mtrl-sci↗

arXiv2020

A novel method for constructing high accurate and robust WENO-Z type scheme

A novel method for constructing robust and high-order accurate weighted essentially non-oscillatory (WENO) scheme is proposed in this paper. The method is mainly based on the WENO-Z type scheme, in which, an eighth-order global smoothness indicator (the square of the approximation of the fourth-order derivative on the five-point stencil used by the fifth-order WENO scheme) is used, and in order to keep the ENO property and robustness, the constant 1 used to calculate the un-normalized weights is replaced by a function of local smoothness indicators of candidate sub-stencils. This function is designed to have following adaptive property: if the five-point stencil contains a discontinuity, then the function approaches to a small value, otherwise, it approaches to a large value. Analysis and numerical results show that the resulted WENO-Z type (WENO-ZN) scheme is robust for capturing shock waves and, in smooth regions, achieves fifth-order accuracy at first-order critical point and fourth-order accuracy at second-order critical point.

math.NA↗

arXiv2020

An efficient targeted ENO scheme with local adaptive dissipation for compressible flow simulation

High fidelity numerical simulation of compressible flow requires the numerical method being used to have both stable shock-capturing capability and high spectral resolution. Recently, a family of Targeted Essentially Non-Oscillatory (TENO) schemes are developed to fulfill such requirements. Although TENO has very low dissipation for smooth flow, it introduces a cutoff value $C_T$ to maintain the non-oscillatory shock-capturing property. $C_T$ is problem depended and therefore needs adjustments by trial and error for different problems. As $C_T$ actually controls the dissipation property of TENO, the choice of $C_T$ for better shock-capturing capability always means higher dissipation. To overcome this, in this paper, a local adaptive method is proposed for the choice of $C_T$. By introducing a novel adaptive function based on the WENO smoothness indicators, $C_T$ is dynamically adjusted form $1.0 \times 10.0^{-10}$ for lower dissipation to $1.0 \times 10.0^{-4}$ for better capturing of shock according to the smoothness of the reconstruction stencil. Numerical results of the new method are compared with those of the original TENO method and the TENO-A method (Fu et al., JCP, 2017). It reveals that the new method is capable of better suppressing numerical oscillations near discontinuities while maintaining the low dissipation property of TENO at lower extra computational cost.

physics.flu-dyn↗

arXiv2020

Excess C/H in Protoplanetary Disk Gas from Icy Pebble Drift across the CO Snowline

The atmospheric composition of giant planets carries the information of their formation history. Superstellar C/H ratios are seen in atmospheres of Jupiter, Saturn, and various giant exoplanets. Also, giant exoplanets show a wide range of C/O ratio. To explain these ratios, one hypothesis is that protoplanets accrete carbon-enriched gas when a large number of icy pebbles drift across the CO snowline. Here we report the first direct evidence of an elevated C/H ratio in disk gas. We use two thermo-chemical codes to model the $^{13}$C$^{18}$O, C$^{17}$O, and C$^{18}$O (2-1) line spectra of the HD 163296 disk. We show that the gas inside the CO snowline ($\sim$70 au) has a C/H ratio of 1-2 times higher than the stellar value. This ratio exceeds the expected value substantially, as only 25-60% of the carbon should be in gas at these radii. Although we cannot rule out the case of a normal C/H ratio inside 70 au, the most probable solution is an elevated C/H ratio of 2-8 times higher than the expectation. Our model also shows that the gas outside 70 au has a C/H ratio of 0.1$\times$ the stellar value. This picture of enriched C/H gas at the inner region and depleted gas at the outer region is consistent with numerical simulations of icy pebble growth and drift in protoplanetary disks. Our results demonstrate that the large-scale drift of icy pebble can occur in disks and may significantly change the disk gas composition for planet formation.

astro-ph.EP↗

arXiv2020

Rapid Evolution of Volatile CO from the Protostellar Disk Stage to the Protoplanetary Disk Stage

Recent observations show that the CO gas abundance, relative to H$_2$, in many 1-10 Myr old protoplanetary disks may be heavily depleted, by a factor of 10-100 compared to the canonical interstellar medium value of 10$^{-4}$. When and how this depletion happens can significantly affect compositions of planetesimals and atmospheres of giant planets. It is therefore important to constrain if the depletion occurs already at the earliest protostellar disk stage. Here we present spatially resolved observations of C$^{18}$O, C$^{17}$O, and $^{13}$C$^{18}$O $J$=2-1 lines in three protostellar disks. We show that the C$^{18}$O line emits from both the disk and the inner envelope, while C$^{17}$O and $^{13}$C$^{18}$O lines are consistent with a disk origin. The line ratios indicate that both C$^{18}$O and C$^{17}$O lines are optically thick in the disk region, and only $^{13}$C$^{18}$O line is optically thin. The line profiles of the $^{13}$C$^{18}$O emissions are best reproduced by Keplerian gaseous disks at similar sizes as their mm-continuum emissions, suggesting small radial separations between the gas and mm-sized grains in these disks, in contrast to the large separation commonly seen in protoplanetary disks. Assuming a gas-to-dust ratio of 100, we find that the CO gas abundances in these protostellar disks are consistent with the ISM abundance within a factor of 2, nearly one order of magnitude higher than the average value of 1-10 Myr old disks. These results suggest that there is a fast, $\sim$1 Myr, evolution of the abundance of CO gas from the protostellar disk stage to the protoplanetary disk stage.

astro-ph.EP↗

arXiv2020

Detection Method Based on Automatic Visual Shape Clustering for Pin-Missing Defect in Transmission Lines

Bolts are the most numerous fasteners in transmission lines and are prone to losing their split pins. How to realize the automatic pin-missing defect detection for bolts in transmission lines so as to achieve timely and efficient trouble shooting is a difficult problem and the long-term research target of power systems. In this paper, an automatic detection model called Automatic Visual Shape Clustering Network (AVSCNet) for pin-missing defect is constructed. Firstly, an unsupervised clustering method for the visual shapes of bolts is proposed and applied to construct a defect detection model which can learn the difference of visual shape. Next, three deep convolutional neural network optimization methods are used in the model: the feature enhancement, feature fusion and region feature extraction. The defect detection results are obtained by applying the regression calculation and classification to the regional features. In this paper, the object detection model of different networks is used to test the dataset of pin-missing defect constructed by the aerial images of transmission lines from multiple locations, and it is evaluated by various indicators and is fully verified. The results show that our method can achieve considerably satisfactory detection effect.

eess.IV↗

arXiv2019

Hybridization-Induced Gapped and Gapless States on the Surfaces of Magnetic Topological Insulators

The layered MnBi2nTe3n+1 family represents the first intrinsic antiferromagnetic topological insulator (AFM TI, protected by a combination symmetry ) ever discovered, providing an ideal platform to explore novel physics such as quantum anomalous Hall effect at elevated temperature and axion electrodynamics. Recent angle-resolved photoemission spectroscopy (ARPES) experiments on this family have revealed that all terminations exhibit (nearly) gapless topological surface states (TSSs) within the AFM state, violating the definition of the AFM TI, as the surfaces being studied should be -breaking and opening a gap. Here we explain this curious paradox using a surface-bulk band hybridization picture. Combining ARPES and first-principles calculations, we prove that only an apparent gap is opened by hybridization between TSSs and bulk bands. The observed (nearly) gapless features are consistently reproduced by tight-binding simulations where TSSs are coupled to a Rashba-split bulk band. The Dirac-cone-like spectral features are actually of bulk origin, thus not sensitive to the-breaking at the AFM surfaces. This picture explains the (nearly) gapless behaviour found in both Bi2Te3- and MnBi2Te4-terminated surfaces and is applicable to all terminations of MnBi2nTe3n+1 family. Our findings highlight the role of band hybridization, superior to magnetism in this case, in shaping the general surface band structure in magnetic topological materials for the first time.

cond-mat.str-el↗

arXiv2019

Unsupervised Detection of Sub-events in Large Scale Disasters

Social media plays a major role during and after major natural disasters (e.g., hurricanes, large-scale fires, etc.), as people ``on the ground'' post useful information on what is actually happening. Given the large amounts of posts, a major challenge is identifying the information that is useful and actionable. Emergency responders are largely interested in finding out what events are taking place so they can properly plan and deploy resources. In this paper we address the problem of automatically identifying important sub-events (within a large-scale emergency ``event'', such as a hurricane). In particular, we present a novel, unsupervised learning framework to detect sub-events in Tweets for retrospective crisis analysis. We first extract noun-verb pairs and phrases from raw tweets as sub-event candidates. Then, we learn a semantic embedding of extracted noun-verb pairs and phrases, and rank them against a crisis-specific ontology. We filter out noisy and irrelevant information then cluster the noun-verb pairs and phrases so that the top-ranked ones describe the most important sub-events. Through quantitative experiments on two large crisis data sets (Hurricane Harvey and the 2015 Nepal Earthquake), we demonstrate the effectiveness of our approach over the state-of-the-art. Our qualitative evaluation shows better performance compared to our baseline.

cs.SI↗

arXiv2019

DeepAuto: A Hierarchical Deep Learning Framework for Real-Time Prediction in Cellular Networks

Accurate real-time forecasting of key performance indicators (KPIs) is an essential requirement for various LTE/5G radio access network (RAN) automation. However, an accurate prediction can be very challenging in large-scale cellular environments due to complex spatio-temporal dynamics, network configuration changes and unavailability of real-time network data. In this work, we introduce a reusable analytics framework that enables real-time KPI prediction using a hierarchical deep learning architecture. Our prediction approach, namely DeepAuto, stacks multiple long short-term memory (LSTM) networks horizontally to capture instantaneous, periodic and seasonal patterns in KPI time-series. It further merge with feed-forward networks to learn the impact of network configurations and other external factors. We validate the approach by predicting two important KPIs, including cell load and radio channel quality, using large-scale real network streaming measurement data from the operator. For cell load prediction, DeepAuto model showed up to 15% improvement in Root Mean Square Error (RMSE) compared to naive method of using recent measurements for short-term horizon and up to 32% improvement for longer-term prediction.

eess.SP↗

arXiv2019

Nanoconfined, dynamic electrolyte gating and memory effects in multilayered graphene-based membranes

Multilayered graphene-based nanoporous membranes with electrolyte incorporated between individual sheets is a unique nano-heterostructure system in which nanoconfined electrons in graphene and ions confined in between sheets are intimately coupled throughout the entire membrane. In contrast to the general notion that the electrolyte gating is unlikely to appear in multilayered graphene stacks, it is demonstrated in this work that the electrolyte gating effect in monolayer graphene can be transferred to its corresponding multilayered porous membranes. This gating effect presented on each individual graphene sheets through electrolyte confined in nanopores provides a real-time, electrical approach for probing the complex dynamics of nanoconfined electrical double layer. This has enabled the observation of the ionic memory effect in supercapacitors and produces new insights into the charging dynamics of supercapacitors. Such discoveries may stimulate the design of novel nanoionic devices.

cond-mat.mes-hall↗

arXiv2019

Gapless surface Dirac cone in antiferromagnetic topological insulator MnBi$_2$Te$_4$

The recent discovered antiferromagnetic topological insulators in Mn-Bi-Te family with intrinsic magnetic ordering have rapidly drawn broad interest since its cleaved surface state is believed to be gapped, hosting the unprecedented axion states with half-integer quantum Hall effect. Here, however, we show unambiguously by using high-resolution angle-resolved photoemission spectroscopy that a gapless Dirac cone at the (0001) surface of MnBi$_2$Te$_4$ exists between the bulk band gap. Such unexpected surface state remains unchanged across the bulk Néel temperature, and is even robust against severe surface degradation, indicating additional topological protection. Through symmetry analysis and $\textit{ab}$-$\textit{initio}$ calculations we consider different types of surface reconstruction of the magnetic moments as possible origins giving rise to such linear dispersion. Our results reveal that the intrinsic magnetic topological insulator hosts a rich platform to realize various topological phases such as topological crystalline insulator and time-reversal-preserved topological insulator, by tuning the magnetic configurations.

cond-mat.mtrl-sci↗

arXiv2019

Controllable Length Control Neural Encoder-Decoder via Reinforcement Learning

Controlling output length in neural language generation is valuable in many scenarios, especially for the tasks that have length constraints. A model with stronger length control capacity can produce sentences with more specific length, however, it usually sacrifices semantic accuracy of the generated sentences. Here, we denote a concept of Controllable Length Control (CLC) for the trade-off between length control capacity and semantic accuracy of the language generation model. More specifically, CLC is to alter length control capacity of the model so as to generate sentence with corresponding quality. This is meaningful in real applications when length control capacity and outputs quality are requested with different priorities, or to overcome unstability of length control during model training. In this paper, we propose two reinforcement learning (RL) methods to adjust the trade-off between length control capacity and semantic accuracy of length control models. Results show that our RL methods improve scores across a wide range of target lengths and achieve the goal of CLC. Additionally, two models LenMC and LenLInit modified on previous length-control models are proposed to obtain better performance in summarization task while still maintain the ability to control length.

cs.CL↗

arXiv2019

Efficient variants of the CMRH method for solving a sequence of multi-shifted non-Hermitian linear systems simultaneously

Multi-shifted linear systems with non-Hermitian coefficient matrices arise in numerical solutions of time-dependent partial/fractional differential equations (PDEs/FDEs), in control theory, PageRank problems, and other research fields. We derive efficient variants of the restarted Changing Minimal Residual method based on the cost-effective Hessenberg procedure (CMRH) for this problem class. Then, we introduce a flexible variant of the algorithm that allows to use variable preconditioning at each iteration to further accelerate the convergence of shifted CMRH. We analyse the performance of the new class of methods in the numerical solution of PDEs and FDEs, also against other multi-shifted Krylov subspace methods.

math.NA↗

arXiv2019

Fine-Grained Age Estimation in the wild with Attention LSTM Networks

Age estimation from a single face image has been an essential task in the field of human-computer interaction and computer vision, which has a wide range of practical application values. Accuracy of age estimation of face images in the wild is relatively low for existing methods, because they only take into account the global features, while neglecting the fine-grained features of age-sensitive areas. We propose a novel method based on our attention long short-term memory (AL) network for fine-grained age estimation in the wild, inspired by the fine-grained categories and the visual attention mechanism. This method combines the residual networks (ResNets) or the residual network of residual network (RoR) models with LSTM units to construct AL-ResNets or AL-RoR networks to extract local features of age-sensitive regions, which effectively improves the age estimation accuracy. First, a ResNets or a RoR model pretrained on ImageNet dataset is selected as the basic model, which is then fine-tuned on the IMDB-WIKI-101 dataset for age estimation. Then, we fine-tune the ResNets or the RoR on the target age datasets to extract the global features of face images. To extract the local features of age-sensitive regions, the LSTM unit is then presented to obtain the coordinates of the agesensitive region automatically. Finally, the age group classification is conducted directly on the Adience dataset, and age-regression experiments are performed by the Deep EXpectation algorithm (DEX) on MORPH Album 2, FG-NET and 15/16LAP datasets. By combining the global and the local features, we obtain our final prediction results. Experimental results illustrate the effectiveness and robustness of the proposed AL-ResNets or AL-RoR for age estimation in the wild, where it achieves better state-of-the-art performance than all other convolutional neural network.

cs.CV↗

arXiv2019

Systematic Variations of CO Gas Abundance with Radius in Gas-rich Protoplanetary Disks

CO is the most widely used gas tracer of protoplanetary disks. Its abundance is usually assumed to be an interstellar ratio throughout the warm molecular layer of the disk. But recent observations of low CO gas abundance in many protoplanetary disks challenge our understanding of physical and chemical evolutions in disks. Here we investigate the CO abundance structures in four well-studied disks and compare their structures with predictions of chemical processing of CO and transport of CO ice-coated dust grains in disks. We use spatially resolved CO isotopologue line observations and detailed thermo-chemical models to derive CO abundance structures. We find that the CO abundance varies with radius by an order of magnitude in these disks. We show that although chemical processes can efficiently reduce the total column of CO gas within 1 Myr under an ISM level of cosmic-ray ionization rate, the depletion mostly occurs at the deep region of a disk. Without sufficient vertical mixing, the surface layer is not depleted enough to reproduce weak CO emissions observed. The radial profiles of CO depletion in three disks are qualitatively consistent with predictions of pebble formation, settling, and drifting in disks. But the dust evolution alone cannot fully explain the high depletion observed in some disks. These results suggest that dust evolution may play a significant role in transporting volatile materials and a coupled chemical-dynamical study is necessary to understand what raw materials are available for planet formation at different distances from the central star.

astro-ph.EP↗