arXiv subjects

Jun Zhang

Publications and source records attributed to Jun Zhang.

At least 901 records · Page 50Linked to original sources

arXiv2020

Imaging and spectral study on the null point of a fan-spine structure during a solar flare

Using the multi-instrument observations, we make the first simultaneous imaging and spectral study on the null point of a fan-spine magnetic topology during a solar flare. When magnetic reconnection occurs at the null point, the fan-spine configuration brightens in the (extreme-)ultraviolet channels. In the H$α$ images, the fan-spine structure is partly filled and outlined by the bi-directional material flows ejected from the reconnection site. The extrapolated coronal magnetic field confirms the existence of the fan-spine topology. Before and after the flare peak, the total velocity of the outflows is estimated to be about 60 km s$^{-1}$. During the flare, the Si IV line profile at the reconnection region is enhanced both in the blue-wing and red-wing. At the flare peak time, the total velocity of the outflows is found to be 144 km s$^{-1}$. Superposed on the Si IV profile, there are several deep absorption lines with the blueshift of several tens of km s$^{-1}$. The reason is inferred to be that the bright reconnection region observed in Si IV channel is located under the cooler material appearing as dark features in the H$α$ line. The blueshifted absorption lines indicate the movement of the cooler material toward the observer. The depth of the absorption lines also depends on the amount of cooler material. These results imply that this kind of spectral profiles can be used as a tool to diagnose the properties of cooler material above reconnection site.

astro-ph.SR↗

arXiv2020

Fairness and efficiency for probabilistic allocations with participation constraints

We propose a notion of fairness for allocation problems in which different agents may have different reservation utilities, stemming from different outside options, or property rights. Fairness is usually understood as the absence of envy, but this can be incompatible with reservation utilities. It is possible that Alice's envy of Bob's assignment cannot be remedied without violating Bob's participation constraint. Instead, we seek to rule out {\em justified envy}, defined as envy for which a remedy would not violate any agent's participation constraint. We show that fairness, meaning the absence of justified envy, can be achieved together with efficiency and individual rationality. We introduce a competitive equilibrium approach with price-dependent incomes obtaining the desired properties.

econ.TH↗

arXiv2020

Compressive Sensing Based Massive Access for IoT Relying on Media Modulation Aided Machine Type Communications

A fundamental challenge of the large-scale Internet-of-Things lies in how to support massive machine-type communications (mMTC). This letter proposes a media modulation based mMTC solution for increasing the throughput, where a massive multi-input multi-output based base station (BS) is used for enhancing the detection performance. For such a mMTC scenario, the reliable active device detection and data decoding pose a serious challenge. By leveraging the sparsity of the uplink access signals of mMTC received at the BS, a compressive sensing based massive access solution is proposed for tackling this challenge. Specifically, we propose a block sparsity adaptive matching pursuit algorithm for detecting the active devices, whereby the block-sparsity of the uplink access signals exhibited across the successive time slots and the structured sparsity of media modulated symbols are exploited for enhancing the detection performance. Moreover, a successive interference cancellation based structured subspace pursuit algorithm is conceived for data demodulation of the active devices, whereby the structured sparsity of media modulation based symbols found in each time slot is exploited for improving the detection performance. Finally, our simulation results verify the superiority of the proposed scheme over state-of-the-art solutions.

cs.IT↗

arXiv2020

DIHARD II is Still Hard: Experimental Results and Discussions from the DKU-LENOVO Team

In this paper, we present the submitted system for the second DIHARD Speech Diarization Challenge from the DKULENOVO team. Our diarization system includes multiple modules, namely voice activity detection (VAD), segmentation, speaker embedding extraction, similarity scoring, clustering, resegmentation and overlap detection. For each module, we explore different techniques to enhance performance. Our final submission employs the ResNet-LSTM based VAD, the Deep ResNet based speaker embedding, the LSTM based similarity scoring and spectral clustering. Variational Bayes (VB) diarization is applied in the resegmentation stage and overlap detection also brings slight improvement. Our proposed system achieves 18.84% DER in Track1 and 27.90% DER in Track2. Although our systems have reduced the DERs by 27.5% and 31.7% relatively against the official baselines, we believe that the diarization task is still very difficult.

eess.AS↗

arXiv2020

Scene Text Recognition via Transformer

Scene text recognition with arbitrary shape is very challenging due to large variations in text shapes, fonts, colors, backgrounds, etc. Most state-of-the-art algorithms rectify the input image into the normalized image, then treat the recognition as a sequence prediction task. The bottleneck of such methods is the rectification, which will cause errors due to distortion perspective. In this paper, we find that the rectification is completely unnecessary. What all we need is the spatial attention. We therefore propose a simple but extremely effective scene text recognition method based on transformer [50]. Different from previous transformer based models [56,34], which just use the decoder of the transformer to decode the convolutional attention, the proposed method use a convolutional feature maps as word embedding input into transformer. In such a way, our method is able to make full use of the powerful attention mechanism of the transformer. Extensive experimental results show that the proposed method significantly outperforms state-of-the-art methods by a very large margin on both regular and irregular text datasets. On one of the most challenging CUTE dataset whose state-of-the-art prediction accuracy is 89.6%, our method achieves 99.3%, which is a pretty surprising result. We will release our source code and believe that our method will be a new benchmark of scene text recognition with arbitrary shapes.

cs.CV↗

arXiv2020

A New Quasi One-Dimensional Compound Ba3TiTe5 and Superconductivity Induced by Pressure

We report systematical studies of a new quasi-one-dimensional (1D) compound Ba3TiTe5 and the high-pressure induced superconductivity therein. Ba3TiTe5 was synthesized at high pressure and high temperature. It crystallizes into a hexagonal structure (P63/mcm), which consists of infinite face-sharing octahedral TiTe6 chains and Te chains along the c axis, exhibiting a strong 1D characteristic structure. The first-principles calculations demonstrate that Ba3TiTe5 is a well-defined 1D conductor and thus, it can be considered a starting point to explore the exotic physics induced by pressure via enhancing the interchain hopping to move the 1D conductor to a high dimensional metal. For Ba3TiTe5, high-pressure techniques were employed to study the emerging physics dependent on interchain hopping, such as the Umklapp scattering effect, spin/charge density wave (SDW/CDW), superconductivity and non-Fermi Liquid behavior. Finally, a complete phase diagram was plotted. The superconductivity emerges from 8.8 GPa, near which the Umklapp gap is mostly suppressed. Tc is enhanced and reaches the maximum ~6 K at about 36.7 GPa, where the spin/charge density wave (SDW/CDW) is completely suppressed, and a non-Fermi Liquid behavior appears. Our results suggest that the appearance of superconductivity is associated with the fluctuation due to the suppression of Umklapp gap and the enhancement of Tc is related with the fluctuation of the SDW/CDW.

cond-mat.supr-con↗

arXiv2020

A Perspective on Deep Learning for Molecular Modeling and Simulations

Deep learning is transforming many areas in science, and it has great potential in modeling molecular systems. However, unlike the mature deployment of deep learning in computer vision and natural language processing, its development in molecular modeling and simulations is still at an early stage, largely because the inductive biases of molecules are completely different from those of images or texts. Footed on these differences, we first reviewed the limitations of traditional deep learning models from the perspective of molecular physics, and wrapped up some relevant technical advancement at the interface between molecular modeling and deep learning. We do not focus merely on the ever more complex neural network models, instead, we emphasize the theories and ideas behind modern deep learning. We hope that transacting these ideas into molecular modeling will create new opportunities. For this purpose, we summarized several representative applications, ranging from supervised to unsupervised and reinforcement learning, and discussed their connections with the emerging trends in deep learning. Finally, we outlook promising directions which may help address the existing issues in the current framework of deep molecular modeling.

physics.comp-ph↗

arXiv2020

Millimeter-Wave Full-Duplex UAV Relay: Joint Positioning, Beamforming, and Power Control

In this paper, a full-duplex unmanned aerial vehicle (FD-UAV) relay is employed to increase the communication capacity of millimeter-wave (mmWave) networks. Large antenna arrays are equipped at the source node (SN), destination node (DN), and FD-UAV relay to overcome the high path loss of mmWave channels and to help mitigate the self-interference at the FD-UAV relay. Specifically, we formulate a problem for maximization of the achievable rate from the SN to the DN, where the UAV position, analog beamforming, and power control are jointly optimized. Since the problem is highly non-convex and involves high-dimensional, highly coupled variable vectors, we first obtain the conditional optimal position of the FD-UAV relay for maximization of an approximate upper bound on the achievable rate in closed form, under the assumption of a line-of-sight (LoS) environment and ideal beamforming. Then, the UAV is deployed to the position which is closest to the conditional optimal position and yields LoS paths for both air-to-ground links. Subsequently, we propose an alternating interference suppression (AIS) algorithm for the joint design of the beamforming vectors and the power control variables. In each iteration, the beamforming vectors are optimized for maximization of the beamforming gains of the target signals and the successive reduction of the interference, where the optimal power control variables are obtained in closed form. Our simulation results confirm the superiority of the proposed positioning, beamforming, and power control method compared to three benchmark schemes. Furthermore, our results show that the proposed solution closely approaches a performance upper bound for mmWave FD-UAV systems.

eess.SP↗

arXiv2020

Single-pixel coherent diffraction imaging

Complex-field imaging is indispensable for numerous applications at wavelengths from X-ray to THz, with amplitude describing transmittance (or reflectivity) and phase revealing intrinsic structure of the target object. Coherent diffraction imaging (CDI) employs iterative phase retrieval algorithms to process diffraction measurements and is the predominant non-interferometric method to image complex fields. However, the working spectrum of CDI is quite narrow, because the diffraction measurements on which it relies require dense array detection with ultra-high dynamic range. Here we report a single-pixel CDI technique that works for a wide waveband. A single-pixel detector instead of an array sensor is employed in the far field for detection. It repeatedly records the DC-only component of the diffracted wavefront scattered from an object as it is illuminated by a sequence of binary modulation patterns. This decreases the measurements' dynamic range by several orders of magnitude. We employ an efficient single-pixel phase-retrieval algorithm to jointly recover the object's 2D amplitude and phase maps from the 1D intensity-only measurements. No a priori object information is needed in the recovery process. We validate the technique's quantitative phase imaging nature using both calibrated phase objects and biological samples, and demonstrate its wide working spectrum with both 488-nm visible light and 980-nm near-infrared light. Our approach paves the way for complex-field imaging in a wider waveband where 2D detector arrays are not available, with broad applications in life and material sciences.

eess.IV↗

arXiv2020

Sparse Optimization for Green Edge AI Inference

With the rapid upsurge of deep learning tasks at the network edge, effective edge artificial intelligence (AI) inference becomes critical to provide low-latency intelligent services for mobile users via leveraging the edge computing capability. In such scenarios, energy efficiency becomes a primary concern. In this paper, we present a joint inference task selection and downlink beamforming strategy to achieve energy-efficient edge AI inference through minimizing the overall power consumption consisting of both computation and transmission power consumption, yielding a mixed combinatorial optimization problem. By exploiting the inherent connections between the set of task selection and group sparsity structural transmit beamforming vector, we reformulate the optimization as a group sparse beamforming problem. To solve this challenging problem, we propose a log-sum function based three-stage approach. By adopting the log-sum function to enhance the group sparsity, a proximal iteratively reweighted algorithm is developed. Furthermore, we establish the global convergence analysis and provide the ergodic worst-case convergence rate for this algorithm. Simulation results will demonstrate the effectiveness of the proposed approach for improving energy efficiency in edge AI inference systems.

cs.IT↗

arXiv2020

Pair production from Reissner-Nordström-anti-de Sitter black holes

We study the pair production of charged scalar particles from the five-dimensional near extremal Reissner- Nordström-Anti de Sitter (RN-AdS5) black hole. The pair production rate and the absorption cross section ratio in the full spacetime are obtained and are shown to have proportional relation with their counterparts in the near horizon region. In addition, the holographic descriptions of the pair production both in the IR CFT in the near horizon region and the UV CFT at the asymptotic spatial boundary of the RN-AdS5 black hole are analyzed in the AdS2/CFT1and AdS5/CFT4correspondences, respectively. This work gives a complete description of scalar pair production in the near extremal RN-AdS5black hole.

hep-th↗

arXiv2020

SIR Model-based Prediction of Infected Population of Coronavirus in Hubei Province

After the sudden outbreak of Coronavirus in Wuhan, continuous and rich data of the epidemic has been made public as the vital fact for decision support in control measures and aggressive implementation of containment strategies and plans. With the further growth and spreading of the virus, future resource allocation and planning under updated strategies and measures rely on careful study of the epidemic data and characteristics for accurate prediction and estimation. By using the SIR model and reported data, key parameters are obtained from least sum of squared errors for an accurate prediction of epidemic trend in the last four weeks.

q-bio.PE↗

arXiv2020

How many infections of COVID-19 there will be in the "Diamond Princess"-Predicted by a virus transmission model based on the simulation of crowd flow

Objectives: Simulate the transmission process of COVID-19 in a cruise ship, and then to judge how many infections there will be in the 3711 people in the "Diamond Princess" and analyze measures that could have prevented mass transmission. Methods: Based on the crowd flow model, the virus transmission rule between pedestrians is established, to simulate the spread of the virus caused by the close contact during pedestrians' daily activities on the cruise ship. Measurements and main results: Three types of simulation scenarios are designed, the Basic scenario focus on the process of virus transmission caused by a virus carrier and the effect of the personal protective measure against the virus. The condition that the original virus carriers had disembarked halfway and more and more people strengthen self-protection are considered in the Self-protection scenario, which would comparatively accord with the actual situation of "Diamond princess" cruise. Control scenario are set to simulate the effect of taking recommended or mandatory measures on virus transmission Conclusions: There are 850~1009 persons (with large probability) who have been infected with COVID-19 during the voyage of "Diamond Princess". The crowd infection percentage would be controlled effectively if the recommended or mandatory measures can be taken immediately during the alert phase of COVID-19 outbreaks.

physics.soc-ph↗

arXiv2020

Dynamic SLAM: The Need For Speed

The static world assumption is standard in most simultaneous localisation and mapping (SLAM) algorithms. Increased deployment of autonomous systems to unstructured dynamic environments is driving a need to identify moving objects and estimate their velocity in real-time. Most existing SLAM based approaches rely on a database of 3D models of objects or impose significant motion constraints. In this paper, we propose a new feature-based, model-free, object-aware dynamic SLAM algorithm that exploits semantic segmentation to allow estimation of motion of rigid objects in a scene without the need to estimate the object poses or have any prior knowledge of their 3D models. The algorithm generates a map of dynamic and static structure and has the ability to extract velocities of rigid moving objects in the scene. Its performance is demonstrated on simulated, synthetic and real-world datasets.

cs.RO↗

arXiv2020

Dynamic Signatures of Black Hole Binaries with Superradiant Clouds

Superradiant clouds may develop around a rotating black hole, if there is a bosonic field with Compton wavelength comparable to the size of the black hole. In this paper, we investigate the effects of the cloud on the orbits of nearby compact objects. In particular, we consider the dynamical friction and the backreaction due to level mixing. Under these interactions, the probability of a black hole dynamically capturing other compact objects, such as stellar mass black holes and neutron stars, is generally enhanced with the presence of the cloud. For extreme mass ratio inspirals and binary stellar mass binary black holes, the cloud-induced orbital modulation may be detected by observing the gravitational waveform using space borne gravitational wave detectors, such as LISA. Interestingly within certain range of boson Compton wavelength, the enhanced capture rate of stellar mass black holes could accelerate hierarchical mergers, with higher-generation merger product being more massive than the mass threshold predicted by supernova pair instability. These observational signatures provide promising ways of searching light bosons with gravitational waves.

gr-qc↗

arXiv2020

Communication-Efficient Edge AI: Algorithms and Systems

Artificial intelligence (AI) has achieved remarkable breakthroughs in a wide range of fields, ranging from speech processing, image classification to drug discovery. This is driven by the explosive growth of data, advances in machine learning (especially deep learning), and easy access to vastly powerful computing resources. Particularly, the wide scale deployment of edge devices (e.g., IoT devices) generates an unprecedented scale of data, which provides the opportunity to derive accurate models and develop various intelligent applications at the network edge. However, such enormous data cannot all be sent from end devices to the cloud for processing, due to the varying channel quality, traffic congestion and/or privacy concerns. By pushing inference and training processes of AI models to edge nodes, edge AI has emerged as a promising alternative. AI at the edge requires close cooperation among edge devices, such as smart phones and smart vehicles, and edge servers at the wireless access points and base stations, which however result in heavy communication overheads. In this paper, we present a comprehensive survey of the recent developments in various techniques for overcoming these communication challenges. Specifically, we first identify key communication challenges in edge AI systems. We then introduce communication-efficient techniques, from both algorithmic and system perspectives for training and inference tasks at the network edge. Potential future research directions are also highlighted.

cs.IT↗

arXiv2020

Chirp effects on pair production in oscillating electric fields with spatial inhomogeneity

Dirac-Heisenberg-Wigner formalism is used to study chirp effects on the vacuum pair creation under inhomogeneous electric fields. For rapidly oscillating electric fields, the particle momentum spectrum is sensitive to both of the spatial scale and the chirp parameter, and the external field width has less significant effect for the maximally large chirp. For slowly oscillating electric fields, chirp effects could be identified at large spatial extents and the carrier phase plays a significant role reflecting chirp effects even at small spatial scales. We also notice that, the local density approximation holds for all external field profiles considered in this work at the quasihomogeneous limit allowing one to use arguments from homogeneous scenarios to analyze inhomogeneous results.

quant-ph↗

arXiv2020

Sympathetic eruptions of two filaments with an identifiable causal link observed by the Solar Dynamics Observatory

Filament eruptions occurring at different places within a relatively short time internal, but with a certain physical causal connection are usually known as sympathetic eruption. Studies on sympathetic eruptions are not uncommon. However, in the existed reports, the causal links between sympathetic eruptions remain rather speculative. In this work, we present detailed observations of a sympathetic filament eruption event, where an identifiable causal link between two eruptive filaments is observed. On 2015 November 15, two filaments (F1 in the north and F2 in the south) were located at the southwestern quadrant of solar disk. The main axes of them were almost parallel to each other. Around 22:20 UT, F1 began to erupt, forming two flare ribbons. The southwestern ribbon apparently moved to southwest and intruded southeast part of F2. This continuous intrusion caused F2's eventual eruption. Accompanying the eruption of F2, flare ribbons and post-flare loops appeared in northwest region of F2. Meanwhile, neither flare ribbons nor post-flare loops could be observed in southeastern area of F2. In addition, the nonlinear force-free field (NLFFF) extrapolations show that the magnetic fields above F2 in the southeast region are much weaker than that in the northwest region. These results imply that the overlying magnetic fields of F2 were not uniform. So we propose that the southwest ribbon formed by eruptive F1 invaded F2 from its southeast region with relatively weaker overlying magnetic fields in comparison with its northwest region, disturbing F2 and leading F2 to erupt eventually.

astro-ph.SR↗