arXiv subjects

Ming Zhang

Publications and source records attributed to Ming Zhang.

At least 325 records · Page 18Linked to original sources

arXiv2021

QoS-aware User Grouping Strategy for Downlink Multi-Cell NOMA Systems

In multi-cell non-orthogonal multiple access (NOMA) systems, designing an appropriate user grouping strategy is an open problem due to diverse quality of service (QoS) requirements and inter-cell interference. In this paper, we exploit both game theory and graph theory to study QoS-aware user grouping strategies, aiming at minimizing power consumption in downlink multi-cell NOMA systems. Under different QoS requirements, we derive the optimal successive interference cancellation (SIC) decoding order with inter-cell interference, which is different from existing SIC decoding order of increasing channel gains, and obtain the corresponding power allocation strategy. Based on this, the exact potential game model of the user grouping strategies adopted by multiple cells is formulated. We prove that, in this game, the problem for each player to find a grouping strategy can be converted into the problem of searching for specific negative loops in the graph composed of users. Bellman-Ford algorithm is expanded to find these negative loops. Furthermore, we design a greedy based suboptimal strategy to approach the optimal solution with polynomial time. Extensive simulations confirm the effectiveness of grouping users with consideration of QoS and inter-cell interference, and show that the proposed strategies can considerably reduce total power consumption comparing with reference strategies.

cs.IT↗

arXiv2021

Disentangle pathways in strong field molecular photoionization byangular distribution of dissociation fragments

In strong field ionization, the pump pulse not only photoionizes the molecule, but also drives efficient population exchanges between its ionic ground and excited states.In this study, we investigated the population dynamics accompanying strong field molecular photoionization, using angular distribution of dissociative fragments after ionization.Our results reveal that the first and higher order processes of the post-ionization population redistribution mechanism (PPRM) in the ion core can be disentangled and classified by {its} angle-resolved kinetic energy release (KER) spectra.We demonstrate that the imprints of PPRM in the KER spectra can be used to determine the branching ratio of the population exchange pathways of different orders, by exploiting the pump intensity dependent variation of the spectra.

physics.chem-ph↗

arXiv2021

Semantic Hierarchy Preserving Deep Hashing for Large-scale Image Retrieval

Deep hashing models have been proposed as an efficient method for large-scale similarity search. However, most existing deep hashing methods only utilize fine-level labels for training while ignoring the natural semantic hierarchy structure. This paper presents an effective method that preserves the classwise similarity of full-level semantic hierarchy for large-scale image retrieval. Experiments on two benchmark datasets show that our method helps improve the fine-level retrieval performance. Moreover, with the help of the semantic hierarchy, it can produce significantly better binary codes for hierarchical retrieval, which indicates its potential of providing more user-desired retrieval results.

cs.CV↗

arXiv2021

DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation

Heterogeneous information network has been widely used to alleviate sparsity and cold start problems in recommender systems since it can model rich context information in user-item interactions. Graph neural network is able to encode this rich context information through propagation on the graph. However, existing heterogeneous graph neural networks neglect entanglement of the latent factors stemming from different aspects. Moreover, meta paths in existing approaches are simplified as connecting paths or side information between node pairs, overlooking the rich semantic information in the paths. In this paper, we propose a novel disentangled heterogeneous graph attention network DisenHAN for top-$N$ recommendation, which learns disentangled user/item representations from different aspects in a heterogeneous information network. In particular, we use meta relations to decompose high-order connectivity between node pairs and propose a disentangled embedding propagation layer which can iteratively identify the major aspect of meta relations. Our model aggregates corresponding aspect features from each meta relation for the target user/item. With different layers of embedding propagation, DisenHAN is able to explicitly capture the collaborative filtering effect semantically. Extensive experiments on three real-world datasets show that DisenHAN consistently outperforms state-of-the-art approaches. We further demonstrate the effectiveness and interpretability of the learned disentangled representations via insightful case studies and visualization.

cs.IR↗

arXiv2021

lenstronomy II: A gravitational lensing software ecosystem

lenstronomy is an Astropy-affiliated Python package for gravitational lensing simulations and analyses. lenstronomy was introduced by Birrer and Amara (2018) and is based on the linear basis set approach by Birrer et a. (2015). The user and developer base of lenstronomy has substantially grown since then, and the software has become an integral part of a wide range of recent analyses, such as measuring the Hubble constant with time-delay strong lensing or constraining the nature of dark matter from resolved and unresolved small scale lensing distortion statistics. The modular design has allowed the community to incorporate innovative new methods, as well as to develop enhanced software and wrappers with more specific aims on top of the lenstronomy API. Through community engagement and involvement, lenstronomy has become a foundation of an ecosystem of affiliated packages extending the original scope of the software and proving its robustness and applicability at the forefront of the strong gravitational lensing community in an open source and reproducible manner.

astro-ph.CO↗

arXiv2021

FNAS: Uncertainty-Aware Fast Neural Architecture Search

Reinforcement learning (RL)-based neural architecture search (NAS) generally guarantees better convergence yet suffers from the requirement of huge computational resources compared with gradient-based approaches, due to the rollout bottleneck -- exhaustive training for each sampled generation on proxy tasks. In this paper, we propose a general pipeline to accelerate the convergence of the rollout process as well as the RL process in NAS. It is motivated by the interesting observation that both the architecture and the parameter knowledge can be transferred between different experiments and even different tasks. We first introduce an uncertainty-aware critic (value function) in Proximal Policy Optimization (PPO) to utilize the architecture knowledge in previous experiments, which stabilizes the training process and reduces the searching time by 4 times. Further, an architecture knowledge pool together with a block similarity function is proposed to utilize parameter knowledge and reduces the searching time by 2 times. It is the first to introduce block-level weight sharing in RLbased NAS. The block similarity function guarantees a 100% hitting ratio with strict fairness. Besides, we show that a simply designed off-policy correction factor used in "replay buffer" in RL optimization can further reduce half of the searching time. Experiments on the Mobile Neural Architecture Search (MNAS) search space show the proposed Fast Neural Architecture Search (FNAS) accelerates standard RL-based NAS process by ~10x (e.g. ~256 2x2 TPUv2 x days / 20,000 GPU x hour -> 2,000 GPU x hour for MNAS), and guarantees better performance on various vision tasks.

cs.LG↗

arXiv2021

MoDL-QSM: Model-based Deep Learning for Quantitative Susceptibility Mapping

Quantitative susceptibility mapping (QSM) has demonstrated great potential in quantifying tissue susceptibility in various brain diseases. However, the intrinsic ill-posed inverse problem relating the tissue phase to the underlying susceptibility distribution affects the accuracy for quantifying tissue susceptibility. Recently, deep learning has shown promising results to improve accuracy by reducing the streaking artifacts. However, there exists a mismatch between the observed phase and the theoretical forward phase estimated by the susceptibility label. In this study, we proposed a model-based deep learning architecture that followed the STI (susceptibility tensor imaging) physical model, referred to as MoDL-QSM. Specifically, MoDL-QSM accounts for the relationship between STI-derived phase contrast induced by the susceptibility tensor terms (ki13,ki23,ki33) and the acquired single-orientation phase. The convolution neural networks are embedded into the physical model to learn a regularization term containing prior information. ki33 and phase induced by ki13 and ki23 terms were used as the labels for network training. Quantitative evaluation metrics (RSME, SSIM, and HFEN) were compared with recently developed deep learning QSM methods. The results showed that MoDL-QSM achieved superior performance, demonstrating its potential for future applications.

cs.CV↗

arXiv2021

New-generation silicon photonics beyond the singlemode regime

The singlemode condition is one of the most important design rules for optical waveguides in guided-wave optics. The reason following the singlemode condition is that higher-order modes might be excited and thus introduce some undesired mode-mismatching loss as well as inter-mode crosstalk when light propagates along an optical waveguide beyond the singlemode regime. As a result, multimode photonic waveguides are usually not allowed. In this paper, we propose the concept of silicon photonics beyond the singlemode regime, developed with low-loss and low-crosstalk light propagation in multimode photonic waveguides with broadened silicon cores. In particular, silicon photonic waveguides with a broadened core region have shown an ultra-low-loss of ~0.1 dB/cm for the fundamental mode even without any special fabrication process. A micro-racetrack resonator fabricated with standard 220-nm-SOI MPW-foundry processes shows a record intrinsic Q-factor as high as 1.02*107 for the first time, corresponding to ultra-low waveguide propagation loss of only 0.065 dB/cm. A high-performance microwave photonic filter on silicon is then realized with an ultra-narrow 3-dB bandwidth of 20.6 MHz as well as a tuning range of ~20 GHz for the first time. An on-chip 100-cm-long delayline is also demonstrated by using the present broadened SOI photonic waveguides with compact Euler-curve bends, the measured propagation loss is ~0.14 dB/cm. The proposed concept of silicon photonics beyond the singlemode regime helps solve the issue of high propagation loss and also significantly reduces the random phase errors of light due to the random variations of waveguide dimensions. In particularity it enables silicon photonic devices with enhanced performances, which paves the way for new-generation silicon photonics realizing the large-scale photonic integration.

physics.optics↗

arXiv2021

IMU Data Processing For Inertial Aided Navigation: A Recurrent Neural Network Based Approach

In this work, we propose a novel method for performing inertial aided navigation, by using deep neural networks (DNNs). To date, most DNN inertial navigation methods focus on the task of inertial odometry, by taking gyroscope and accelerometer readings as input and regressing for integrated IMU poses (i.e., position and orientation). While this design has been successfully applied on a number of applications, it is not of theoretical performance guarantee unless patterned motion is involved. This inevitably leads to significantly reduced accuracy and robustness in certain use cases. To solve this problem, we design a framework to compute observable IMU integration terms using DNNs, followed by the numerical pose integration and sensor fusion to achieve the performance gain. Specifically, we perform detailed analysis on the motion terms in IMU kinematic equations, propose a dedicated network design, loss functions, and training strategies for the IMU data processing, and conduct extensive experiments. The results show that our method is generally applicable and outperforms both traditional and DNN methods by wide margins.

cs.RO↗

arXiv2021

RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation

We present RangeRCNN, a novel and effective 3D object detection framework based on the range image representation. Most existing methods are voxel-based or point-based. Though several optimizations have been introduced to ease the sparsity issue and speed up the running time, the two representations are still computationally inefficient. Compared to them, the range image representation is dense and compact which can exploit powerful 2D convolution. Even so, the range image is not preferred in 3D object detection due to scale variation and occlusion. In this paper, we utilize the dilated residual block (DRB) to better adapt different object scales and obtain a more flexible receptive field. Considering scale variation and occlusion, we propose the RV-PV-BEV (range view-point view-bird's eye view) module to transfer features from RV to BEV. The anchor is defined in BEV which avoids scale variation and occlusion. Neither RV nor BEV can provide enough information for height estimation; therefore, we propose a two-stage RCNN for better 3D detection performance. The aforementioned point view not only serves as a bridge from RV to BEV but also provides pointwise features for RCNN. Experiments show that RangeRCNN achieves state-of-the-art performance on the KITTI dataset and the Waymo Open dataset, and provides more possibilities for real-time 3D object detection. We further introduce and discuss the data augmentation strategy for the range image based method, which will be very valuable for future research on range image.

cs.CV↗

arXiv2021

Phase transition and Quasinormal modes for Charged black holes in 4D Einstein-Gauss-Bonnet gravity

In this paper, we study the quasinormal modes(QNMs) of massless scalar perturbations to probe the Van der Waals like small and large black holes(SBH/LBH) phase transition of (charged) AdS black holes in the 4-dimensional Einstein Gauss-Bonnet gravity. We find that the signature of this SBH/LBH phase transition in the isobaric process can be detected since the slopes of the QNMs frequencies change drastically different in small and large black holes near the critical point. The obtained results further support that the QNMs can be a dynamic probe of the thermodynamic properties in black holes.

hep-th↗

arXiv2021

NUT charges and black hole shadows

We study the nontrivial effects of the NUT charges on the shadows of the Kerr-Taub-NUT black holes seen by zero-angular-momentum-observers. Inclination angles with which the observers gain maximal, locally extreme or minimal shadow sizes and distortions are investigated for the black holes with different NUT charges and distributions of string singularities. Typically, we discover that when the observer approaches the string singularity, the shadow size can be relatively very small while the shadow distortion being relatively quite large. And exceptionally, we find that when the string singularity locates only at the south pole axis of the black hole with large enough NUT charge, the shadow size is maximal for a north pole observer.

gr-qc↗

arXiv2021

Improved Deep Classwise Hashing With Centers Similarity Learning for Image Retrieval

Deep supervised hashing for image retrieval has attracted researchers' attention due to its high efficiency and superior retrieval performance. Most existing deep supervised hashing works, which are based on pairwise/triplet labels, suffer from the expensive computational cost and insufficient utilization of the semantics information. Recently, deep classwise hashing introduced a classwise loss supervised by class labels information alternatively; however, we find it still has its drawback. In this paper, we propose an improved deep classwise hashing, which enables hashing learning and class centers learning simultaneously. Specifically, we design a two-step strategy on center similarity learning. It interacts with the classwise loss to attract the class center to concentrate on the intra-class samples while pushing other class centers as far as possible. The centers similarity learning contributes to generating more compact and discriminative hashing codes. We conduct experiments on three benchmark datasets. It shows that the proposed method effectively surpasses the original method and outperforms state-of-the-art baselines under various commonly-used evaluation metrics for image retrieval.

cs.CV↗

arXiv2021

Null hypersurface caustics and super-entropic black holes

We obtain a charged, rotating and accelerating black hole solution in the $f(R)$ gravity and calculate thermodynamic quantities of the black hole in the slow acceleration regime. We then find that the black hole can be super-entropic in a certain condition. After investigating the null hypersurface of the black hole, we show that there exist super-entropic black holes whose null hypersurface caustics only form inside the Cauchy horizon.

gr-qc↗

arXiv2021

Topologically protected valley-dependent quantum photonic circuits

Topological photonics has been introduced as a powerful platform for integrated optics, since it can deal with robust light transport, and be further extended to the quantum world. Strikingly, valley-contrasting physics in topological photonic structures contributes to valley-related edge states, their unidirectional coupling, and even valley-dependent wave-division in topological junctions. Here, we design and fabricate nanophotonic topological harpoon-shaped beam splitters (HSBSs) based on $120$-deg-bending interfaces and demonstrate the first on-chip valley-dependent quantum information process. Two-photon quantum interference, namely, HongOu-Mandel (HOM) interference with a high visibility of $0.956 \pm 0.006$, is realized with our 50/50 HSBS, which is constructed by two topologically distinct domain walls. Cascading this kind of HSBS together, we also demonstrate a simple quantum photonic circuit and generation of a path-entangled state. Our work shows that the photonic valley state can be used in quantum information processing, and it is possible to realize more complex quantum circuits with valley-dependent photonic topological insulators, which provides a novel method for on-chip quantum information processing.

quant-ph↗

arXiv2021

Constraints on High-Order Gravitational Theories from Weak Cosmic Censorship

Weak cosmic censorship conjecture (WCCC) is a basic principle that guarantees the predictability of spacetime and should be valid in any classical theories. One critical scientific question is whether the WCCC can serve as a constraint to the gravitational theories. To explore this question, we perform the first-order Sorce-Wald's gedanken experiments to test the WCCC in the higher-order gravitational theories and find that there exists a destruction condition $S_\text{ext}'(r_h)<0$ for the extremal black holes. To show the power of this condition, we evaluate the constraints given by WCCC in the quadratic and cubic gravitational theories. Our investigation makes an essential step toward applying WCCC to constrain the modified gravitational theories, and opens a new avenue to judge which theory is reasonable.

gr-qc↗

arXiv2021

Time-dependent Clearance of Cyclosporine in Adult Renal Transplant Recipients: A Population Pharmacokinetic Perspective

Aim The pharmacokinetic (PK) properties of cyclosporine (CsA) in renal transplant recipients are patient- and time-dependent. Knowledge of this time-related variability is necessary to maintain or achieve CsA target exposure. Here, we aimed to identify factors explaining variabilities in CsA PK properties and characterise time-dependent clearance (CL/F) by performing a comprehensive analysis of CsA PK factors using population PK (popPK) modelling of long-term follow-up data from our institution. Methods In total, 3,674 whole-blood CsA concentrations from 183 patients who underwent initial renal transplantation were analysed using nonlinear mixed-effects modelling. The effects of potential covariates were selected according to a previous report and well-accepted theoretical mechanisms. Model-informed individualised therapeutic regimens were also conducted. Results A two-compartment model adequately described the data and the estimated mean CsA CL/F was 32.6 L h-1 (5%). Allometrically scaled body size, haematocrit (HCT) level, CGC haplotype carrier status, and postoperative time may contribute to CsA PK variability. The CsA bioavailability in patients receiving a prednisolone dose (PD) of 80 mg was 20.6% lower than that in patients receiving 20 mg. A significant decrease (52.6%) in CL/F was observed as the HCT increased from 10.5% to 60.5%. The CL/F of the non-CGC haplotype carrier was 14.4% lower than that of the CGC haplotype carrier at 3 months post operation. CsA dose adjustments should be considered in different postoperative periods. Conclusions By monitoring body size, HCT, PD, and CGC haplotype, changes in CsA CL/F over time could be predicted. Such information could be used to optimise CsA therapy.

q-bio.TO↗

arXiv2021

Comment on "Constraints on Low-Energy Effective Theories from Weak Cosmic Censorship"

Recently, it was argued in [Phys. Rev. Lett. {\bf126}, 031102 (2021)] that the WCCC can serve as a constraint to high-order effective field theories. However, we find there exists a key error in their approximate black hole solution. After correcting it, their calculation cannot show the ability of WCCC to constrain the gravitational theories.

gr-qc↗