arXiv subjects

Fan Zhang

Publications and source records attributed to Fan Zhang.

At least 703 records · Page 39Linked to original sources

arXiv2021

Gain stabilization and consistency correction approach for multiple SiPM-based gamma-ray detectors on GECAM

Each satellite of the Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM, mission) consists of 25 SiPM based gamma-ray detectors (GRDs). Although SiPM based GRD has merits of compact size and low bias-voltage, the drift of the SiPM gain with temperature is a severe problem for GRD performance. An adaptive voltage supply source was designed to automatically adjust the SiPM bias voltage to compensate the temperature effects and keep the gain stable. This approach has been proved to be effective during both the on-ground and in-flight tests. The in-flight measured variation of the SiPM gain is within 2%. To reduce the gain non-uniformity of GRDs, an iterative bias voltage adjustment approach is proposed and implemented. The gain non-uniformity is reduced from 17% to 0.6%. In this paper, the gain stabilization and consistency correction approach are presented and discussed in detail.

physics.ins-det↗

arXiv2021

GECAM detection of a bright type-I X-ray burst from 4U 0614+09: confirmation its spin frequency at 413 Hz

One month after launching Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM), a bright thermonuclear X-ray burst from 4U~0614+09, was observed on January 24, 2021. We report the time-resolved spectroscopy of the burst and a burst oscillation detection at 413 Hz with a fractional amplitude 3.4\% (rms). This coincides with the burst oscillation previously discovered with \textit{Swift}/BAT \citep{Strohmayer2008}, and therefore confirms the spin frequency of this source. This burst is the brightest one in the normal bursts (except the superburst) ever detected from 4U~0614+09, which leads to an upper limit of distance estimation as 3.1 kpc. The folded light curve during the burst oscillation shows a multi-peak structure, which is the first case observed during a single burst oscillation in nonpulsating sources. The multi-peak profile could be due to additional harmonics of the burst oscillation, which is corresponding to several brighter/fainter spots at the stellar surface.

astro-ph.HE↗

arXiv2021

ViSTRA3: Video Coding with Deep Parameter Adaptation and Post Processing

This paper presents a deep learning-based video compression framework (ViSTRA3). The proposed framework intelligently adapts video format parameters of the input video before encoding, subsequently employing a CNN at the decoder to restore their original format and enhance reconstruction quality. ViSTRA3 has been integrated with the H.266/VVC Test Model VTM 14.0, and evaluated under the Joint Video Exploration Team Common Test Conditions. Bjønegaard Delta (BD) measurement results show that the proposed framework consistently outperforms the original VVC VTM, with average BD-rate savings of 1.8% and 3.7% based on the assessment of PSNR and VMAF.

eess.IV↗

arXiv2021

A magnetospheric dichotomy for pulsars with extreme inclinations

Expanding on the comment by Lyne et al (2017), that intermittent pulsars tend to congregate near a stripe in the logarithmic period versus period-derivative diagram, representing a small range of polar cap electric potential, as well as the fact (already apparent in their Fig.~7, but not explicitly stated there) that high-fraction nulling pulsars also tend to reside within this and an additional stripe, we make the observation that the two stripes further match the "death lines" for double and single-pole interpulses, associated with nearly orthogonal and aligned rotators respectively. These extreme inclinations are known to suffer from pair production deficiencies, so we propose to explain intermittency and high-fraction nulling by reinvigorating some older quiescent (no pulsar wind or radio emission) "electrosphere" solutions. Specifically, as the polar potential drops below the two threshold bands (i.e., the two stripes), corresponding to the aligned and orthogonal rotators, their respective magnetospheres transition from being of the active pair-production-sustained type into becoming the electrospheres, in which charges are only lifted from the star. The borderline cases sitting in the gap outside of the stable regime of either case manifest as high-fraction nullers. Hall evolution of the magnetic field inside orthogonally rotating neutron stars can furthermore drive secular regime changes, resulting in intermittent pulsars.

astro-ph.HE↗

arXiv2021

Can Graph Neural Networks Learn to Solve MaxSAT Problem?

With the rapid development of deep learning techniques, various recent work has tried to apply graph neural networks (GNNs) to solve NP-hard problems such as Boolean Satisfiability (SAT), which shows the potential in bridging the gap between machine learning and symbolic reasoning. However, the quality of solutions predicted by GNNs has not been well investigated in the literature. In this paper, we study the capability of GNNs in learning to solve Maximum Satisfiability (MaxSAT) problem, both from theoretical and practical perspectives. We build two kinds of GNN models to learn the solution of MaxSAT instances from benchmarks, and show that GNNs have attractive potential to solve MaxSAT problem through experimental evaluation. We also present a theoretical explanation of the effect that GNNs can learn to solve MaxSAT problem to some extent for the first time, based on the algorithmic alignment theory.

cs.AI↗

arXiv2021

Quantum cascade of new correlated phases in trigonally warped bilayer graphene

Divergent density of states offers the unique opportunity to explore a wide variety of correlated electron physics. In the thinnest limit, this has been predicted and verified in the ultra-flat bands of magic-angle twisted bilayer graphene, the band touching points of few-layer rhombohedral graphite, and the lightly doped rhombohedral trilayer graphene. The simpler and seemingly better understood Bernal bilayer graphene is also susceptible to orbital magnetism-driven phases at charge neutrality, such as layer antiferromagnet and quantum anomalous Hall octet. Here we report the discovery of a cascade of novel correlated phases in the vicinity of electric-field-controlled Lifshitz transitions and van Hove singularities in trigonally warped bilayer graphene. We provide compelling evidence for the observation of Stoner ferromagnets - half and quarter metals. More prominently, we identify signatures consistent with a topologically nontrivial Wigner-Hall crystal at zero magnetic field and its transition to a trivial Wigner crystal, as well as two correlated metals whose behavior deviates from standard Fermi liquids. Our results in this reproducible, tunable, simple system opens a new chapter for studying strongly correlated electrons.

cond-mat.mes-hall↗

arXiv2021

Simplex Initialization: A Survey of Techniques and Trends

The simplex method is one of the most fundamental technologies for solving linear programming (LP) problems and has been widely applied to different practical applications. In the past literature, how to improve and accelerate the simplex method has attracted plenty of research. One important way to achieve this goal is to find a better initialization method for the simplex. In this survey, we aim to provide an overview about the initialization methods in the primal and dual simplex, respectively. We also propose several potential future directions about how to improve the existing initialization methods with the help of advanced learning technologies.

math.OC↗

arXiv2021

Branch and Bound in Mixed Integer Linear Programming Problems: A Survey of Techniques and Trends

In this paper, we surveyed the existing literature studying different approaches and algorithms for the four critical components in the general branch and bound (B&B) algorithm, namely, branching variable selection, node selection, node pruning, and cutting-plane selection. However, the complexity of the B&B algorithm always grows exponentially with respect to the increase of the decision variable dimensions. In order to improve the speed of B&B algorithms, learning techniques have been introduced in this algorithm recently. We further surveyed how machine learning can be used to improve the four critical components in B&B algorithms. In general, a supervised learning method helps to generate a policy that mimics an expert but significantly improves the speed. An unsupervised learning method helps choose different methods based on the features. In addition, models trained with reinforcement learning can beat the expert policy, given enough training and a supervised initialization. Detailed comparisons between different algorithms have been summarized in our survey. Finally, we discussed some future research directions to accelerate and improve the algorithms further in the literature.

cs.LG↗

arXiv2021

Crystal-like Order Stabilizing Glasses: Structural Origin of Ultra-stable Metallic Glasses

Glasses are featured with a disordered amorphous structure, being opposite to crystals that are constituted by periodic lattices. In this study we report that the exceptional thermodynamic and kinetic stability of an ultra-stable binary ZrCu metallic glass, fabricated by high-temperature physical vapor deposition, originates from ubiquitous crystal-like medium range order (MRO) constituted by Voronoi polyhedron ordering with well-defined local translational symmetry beyond nearest atomic neighbors. The crystal-like MRO significantly improves the thermodynamic and kinetic stability of the glass, which is in opposition to the conventional wisdom that crystal-like order deteriorates the stability and forming ability of metallic glasses. This study unveils the structural origin of ultra-stable metallic glasses and shines a light on the intrinsic correlation of local atomic structure ordering with glass transition of metallic glasses.

cond-mat.dis-nn↗

arXiv2021

Optimal Discrete Constellation Inputs for Aggregated LiFi-WiFi Networks

In this paper, we investigate the performance of a practical aggregated LiFi-WiFi system with the discrete constellation inputs from a practical view. We derive the achievable rate expressions of the aggregated LiFi-WiFi system for the first time. Then, we study the rate maximization problem via optimizing the constellation distribution and power allocation jointly. Specifically, a multilevel mercy-filling power allocation scheme is proposed by exploiting the relationship between the mutual information and minimum mean-squared error (MMSE) of discrete inputs. Meanwhile, an inexact gradient descent method is proposed for obtaining the optimal probability distributions. To strike a balance between the computational complexity and the transmission performance, we further develop a framework that maximizes the lower bound of the achievable rate where the optimal power allocation can be obtained in closed forms and the constellation distributions problem can be solved efficiently by Frank-Wolfe method. Extensive numerical results show that the optimized strategies are able to provide significant gains over the state-of-the-art schemes in terms of the achievable rate.

cs.IT↗

arXiv2021

Efficient Context-Aware Network for Abdominal Multi-organ Segmentation

The contextual information, presented in abdominal CT scan, is relative consistent. In order to make full use of the overall 3D context, we develop a whole-volume-based coarse-to-fine framework for efficient and effective abdominal multi-organ segmentation. We propose a new efficientSegNet network, which is composed of basic encoder, slim decoder and efficient context block. For the decoder module, anisotropic convolution with a k*k*1 intra-slice convolution and a 1*1*k inter-slice convolution, is designed to reduce the computation burden. For the context block, we propose strip pooling module to capture anisotropic and long-range contextual information, which exists in abdominal scene. Quantitative evaluation on the FLARE2021 validation cases, this method achieves the average dice similarity coefficient (DSC) of 0.895 and average normalized surface distance (NSD) of 0.775. This method won the 1st place on the 2021-MICCAI-FLARE challenge. Codes and models are available at https://github.com/Shanghai-Aitrox-Technology/EfficientSegmentation.

eess.IV↗

arXiv2021

Towards User Engagement Dynamics in Social Networks

The engagement of each user in a social network is an essential indicator for maintaining a sustainable service. Existing studies use the $coreness$ of a user to well estimate its static engagement in a network. However, when the engagement of a user is weakened or strengthened, the influence on other users' engagement is unclear. Besides, the dynamic of user engagement has not been well captured for evolving social networks. In this paper, we systematically study the network dynamic against the engagement change of each user for the first time. The influence of a user is monitored via two novel concepts: the $collapsed~power$ to measure the effect of user weakening, and the $anchored~power$ to measure the effect of user strengthening. We show that the two concepts can be naturally integrated such that a unified offline algorithm is proposed to compute both the collapsed and anchored followers for each user. When the network structure evolves, online techniques are designed to maintain the users' followers, which is faster than redoing the offline algorithm by around 3 orders of magnitude. Extensive experiments on real-life data demonstrate the effectiveness of our model and the efficiency of our algorithms.

cs.SI↗

arXiv2021

Grasp-Oriented Fine-grained Cloth Segmentation without Real Supervision

Automatically detecting graspable regions from a single depth image is a key ingredient in cloth manipulation. The large variability of cloth deformations has motivated most of the current approaches to focus on identifying specific grasping points rather than semantic parts, as the appearance and depth variations of local regions are smaller and easier to model than the larger ones. However, tasks like cloth folding or assisted dressing require recognising larger segments, such as semantic edges that carry more information than points. The first goal of this paper is therefore to tackle the problem of fine-grained region detection in deformed clothes using only a depth image. As a proof of concept, we implement an approach for T-shirts, and define up to 6 semantic regions of varying extent, including edges on the neckline, sleeve cuffs, and hem, plus top and bottom grasping points. We introduce a U-net based network to segment and label these parts. The second contribution of our work is concerned with the level of supervision that we require to train the proposed network. While most approaches learn to detect grasping points by combining real and synthetic annotations, in this work we defy the limitations of the synthetic data, and propose a multilayered domain adaptation (DA) strategy that does not use real annotations at all. We thoroughly evaluate our approach on real depth images of a T-shirt annotated with fine-grained labels. We show that training our network solely with synthetic data and the proposed DA yields results competitive with models trained on real data.

cs.CV↗

arXiv2021

Coded Computation across Shared Heterogeneous Workers with Communication Delay

Distributed computing enables large-scale computation tasks to be processed over multiple workers in parallel. However, the randomness of communication and computation delays across workers causes the straggler effect, which may degrade the performance. Coded computation helps to mitigate the straggler effect, but the amount of redundant load and their assignment to the workers should be carefully optimized. In this work, we consider a multi-master heterogeneous-worker distributed computing scenario, where multiple matrix multiplication tasks are encoded and allocated to workers for parallel computation. The goal is to minimize the communication plus computation delay of the slowest task. We propose worker assignment, resource allocation and load allocation algorithms under both dedicated and fractional worker assignment policies, where each worker can process the encoded tasks of either a single master or multiple masters, respectively. Then, the non-convex delay minimization problem is solved by employing the Markov's inequality-based approximation, Karush-Kuhn-Tucker conditions, and successive convex approximation methods. Through extensive simulations, we show that the proposed algorithms can reduce the task completion delay compared to the benchmarks, and observe that dedicated and fractional worker assignment policies have different scopes of applications.

cs.DC↗

arXiv2021

Higher-order Dirac sonic crystals

Discovering new topological phases of matter is a major theme in fundamental physics and materials science. Dirac semimetal provides an exceptional platform for exploring topological phase transitions under symmetry breaking. Recent theoretical studies have revealed that a three-dimensional Dirac semimetal can harbor fascinating hinge states, a higher-order topological manifestation not known before. However, its realization in experiment is yet to be achieved. In this Letter, we propose a minimum model to construct a spinless higher-order Dirac semimetal protected by C_6v symmetry. By breaking different symmetries, this parent phase transitions into a variety of novel topological phases including higher-order topological insulator, higher-order Weyl semimetal, and higher-order nodal-ring semimetal. Furthermore, for the first time, we experimentally realize this unprecedented higher-order topological phase in a sonic crystal and present an unambiguous observation of the desired hinge states via momentun-space spectroscopy and real-space visualization. Our findings may offer new opportunities to manipulate classical waves such as sound and light.

cond-mat.other↗

arXiv2021

Atomic-Scale Visualization and Manipulation of Domain boundaries in 2D Ferroelectric In2Se3

Domain boundaries in ferroelectric materials exhibit rich and diverse physical properties distinct from their parent materials and have been proposed for novel applications in nanoelectronics and quantum information technology. Due to their complexity and diversity, the internal atomic and electronic structure of domain boundaries that governs the electronic properties as well as the kinetics of domain switching remains far from being elucidated. By using scanning tunneling microscopy and spectroscopy (STM/S) combined with density functional theory (DFT) calculations, we directly visualize the atomic structure of domain boundaries in two-dimensional (2D) ferroelectric beta' In2Se3 down to the monolayer limit and reveal a double-barrier energy potential of the 60° tail to tail domain boundaries for the first time. We further controllably manipulate the domain boundaries with atomic precision by STM and show that the movements of domain boundaries can be driven by the electric field from an STM tip and proceed by the collective shifting of atoms at the domain boundaries. The results will deepen our understanding of domain boundaries in 2D ferroelectric materials and stimulate innovative applications of these materials.

cond-mat.mes-hall↗

arXiv2021

OpenClinicalAI: enabling AI to diagnose diseases in real-world clinical settings

This paper quantitatively reveals the state-of-the-art and state-of-the-practice AI systems only achieve acceptable performance on the stringent conditions that all categories of subjects are known, which we call closed clinical settings, but fail to work in real-world clinical settings. Compared to the diagnosis task in the closed setting, real-world clinical settings pose severe challenges, and we must treat them differently. We build a clinical AI benchmark named Clinical AIBench to set up real-world clinical settings to facilitate researches. We propose an open, dynamic machine learning framework and develop an AI system named OpenClinicalAI to diagnose diseases in real-world clinical settings. The first versions of Clinical AIBench and OpenClinicalAI target Alzheimer's disease. In the real-world clinical setting, OpenClinicalAI significantly outperforms the state-of-the-art AI system. In addition, OpenClinicalAI develops personalized diagnosis strategies to avoid unnecessary testing and seamlessly collaborates with clinicians. It is promising to be embedded in the current medical systems to improve medical services.

cs.AI↗

arXiv2021

Nonconvex and Nonsmooth Sparse Optimization via Adaptively Iterative Reweighted Methods

We propose a general formulation of nonconvex and nonsmooth sparse optimization problems with convex set constraint, which can take into account most existing types of nonconvex sparsity-inducing terms, bringing strong applicability to a wide range of applications. We design a general algorithmic framework of iteratively reweighted algorithms for solving the proposed nonconvex and nonsmooth sparse optimization problems, which solves a sequence of weighted convex regularization problems with adaptively updated weights. First-order optimality condition is derived and global convergence results are provided under loose assumptions, making our theoretical results a practical tool for analyzing a family of various reweighted algorithms. The effectiveness and efficiency of our proposed formulation and the algorithms are demonstrated in numerical experiments on various sparse optimization problems.

cs.IT↗