arXiv subjects

Lin Zhang

Publications and source records attributed to Lin Zhang.

At least 307 records · Page 17Linked to original sources

arXiv2021

A comparison of two approaches for measuring interdisciplinary research output: the disciplinary diversity of authors vs the disciplinary diversity of the reference list

This study investigates the convergence of two bibliometric approaches to the measurement of interdisciplinary research: one based on analyzing disciplinary diversity in the reference list of publications, the other based on the disciplinary diversity of authors of publications. In particular we measure the variety, balance, disparity and integrated diversity index of, respectively, single-author, multi-author single-field, and multi-author multi-field publications. We find that, in general, the diversity of the reference list grows with the number of fields reflected in a paper's authors' list and, to a lesser extent, with the number of authors being equal the number of fields. Further, we find that when fields belonging to different disciplines are reflected in the authors' list, the disparity in the reference list is higher than in the case of fields belonging to the same discipline. However, this general tendency varies across disciplines, and noticeable exceptions are found at individual paper level.

cs.DL↗

arXiv2021

Safe Multi-Agent Reinforcement Learning through Decentralized Multiple Control Barrier Functions

Multi-Agent Reinforcement Learning (MARL) algorithms show amazing performance in simulation in recent years, but placing MARL in real-world applications may suffer safety problems. MARL with centralized shields was proposed and verified in safety games recently. However, centralized shielding approaches can be infeasible in several real-world multi-agent applications that involve non-cooperative agents or communication delay. Thus, we propose to combine MARL with decentralized Control Barrier Function (CBF) shields based on available local information. We establish a safe MARL framework with decentralized multiple CBFs and develop Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to Multi-Agent Deep Deterministic Policy Gradient with decentralized multiple Control Barrier Functions (MADDPG-CBF). Based on a collision-avoidance problem that includes not only cooperative agents but obstacles, we demonstrate the construction of multiple CBFs with safety guarantees in theory. Experiments are conducted and experiment results verify that the proposed safe MARL framework can guarantee the safety of agents included in MARL.

cs.MA↗

arXiv2021

Learning Ultrasound Rendering from Cross-Sectional Model Slices for Simulated Training

Purpose. Given the high level of expertise required for navigation and interpretation of ultrasound images, computational simulations can facilitate the training of such skills in virtual reality. With ray-tracing based simulations, realistic ultrasound images can be generated. However, due to computational constraints for interactivity, image quality typically needs to be compromised. Methods. We propose herein to bypass any rendering and simulation process at interactive time, by conducting such simulations during a non-time-critical offline stage and then learning image translation from cross-sectional model slices to such simulated frames. We use a generative adversarial framework with a dedicated generator architecture and input feeding scheme, which both substantially improve image quality without increase in network parameters. Integral attenuation maps derived from cross-sectional model slices, texture-friendly strided convolutions, providing stochastic noise and input maps to intermediate layers in order to preserve locality are all shown herein to greatly facilitate such translation task. Results. Given several quality metrics, the proposed method with only tissue maps as input is shown to provide comparable or superior results to a state-of-the-art that uses additional images of low-quality ultrasound renderings. An extensive ablation study shows the need and benefits from the individual contributions utilized in this work, based on qualitative examples and quantitative ultrasound similarity metrics. To that end, a local histogram statistics based error metric is proposed and demonstrated for visualization of local dissimilarities between ultrasound images.

eess.IV↗

arXiv2021

Average skew information-based coherence and its typicality for random quantum states

We study the average skew information-based coherence for both random pure and mixed states. The explicit formulae of the average skew information-based coherence are derived and shown to be the functions of the dimension N of the state space. We demonstrate that as N approaches to infinity, the average coherence is 1 for random pure states, and a positive constant less than 1/2 for random mixed states. We also explore the typicality of average skew information-based coherence of random quantum states. Furthermore, we identify a coherent subspace such that the amount of the skew information-based coherence for each pure state in this subspace can be bounded from below almost always by a fixed number that is arbitrarily close to the typical value of coherence.

quant-ph↗

arXiv2020

Reproducing Scientific Experiment with Cloud DevOps

The reproducibility of scientific experiment is vital for the advancement of disciplines based on previous work. To achieve this goal, many researchers focus on complex methodology and self-invented tools which have difficulty in practical usage. In this article, we introduce the Cloud DevOps infrastructure from software engineering community and shows how it can be used effectively for heterogeneous agents to reproduce experiments for computer science related disciplines. DevOps can be enabled using freely available cloud computing machines for medium-sized experiment and self-hosted computing engines for large-scale computing, thus powering researchers to share their experiment result with others in a more reliable way.

cs.DC↗

arXiv2020

Dynamically characterizing topological phases by high-order topological charges

We propose a new theory to characterize equilibrium topological phase with non-equilibrium quantum dynamics by introducing the concept of high-order topological charges, with novel phenomena being predicted. Through a dimension reduction approach, we can characterize a $d$-dimensional ($d$D) integer-invariant topological phase with lower-dimensional topological number quantified by high-order topological charges, of which the $s$th-order topological charges denote the monopoles confined on the $(s-1)$th-order band inversion surfaces (BISs) that are $(d-s+1)$D momentum subspaces. The bulk topology is determined by the $s$th order topological charges enclosed by the $s$th-order BISs. By quenching the system from trivial phase to topological regime, we show that the bulk topology of post-quench Hamiltonian can be detected through a high-order dynamical bulk-surface correspondence, in which both the high-order topological charges and high-order BISs are identified from quench dynamics. This characterization theory has essential advantages in two aspects. First, the highest ($d$th) order topological charges are characterized by only discrete signs of spin-polarization in zero dimension (i.e. the $0$th Chern numbers), whose measurement is much easier than the $1$st-order topological charges that are characterized by the continuous charge-related spin texture in higher dimensional space. Secondly, a more striking result is that a first-order high integer-valued topological charge always reduces to multiple highest-order topological charges with unit charge value, and the latter can be readily detected in experiment. The two fundamental features greatly simplify the characterization and detection of the topological charges and also topological phases, which shall advance the experimental studies in the near future.

cond-mat.str-el↗

arXiv2020

A Multi-intersection Vehicular Cooperative Control based on End-Edge-Cloud Computing

Cooperative Intelligent Transportation Systems (C-ITS) will change the modes of road safety and traffic management, especially at intersections without traffic lights, namely unsignalized intersections. Existing researches focus on vehicle control within a small area around an unsignalized intersection. In this paper, we expand the control domain to a large area with multiple intersections. In particular, we propose a Multi-intersection Vehicular Cooperative Control (MiVeCC) to enable cooperation among vehicles in a large area with multiple unsignalized intersections. Firstly, a vehicular end-edge-cloud computing framework is proposed to facilitate end-edge-cloud vertical cooperation and horizontal cooperation among vehicles. Then, the vehicular cooperative control problems in the cloud and edge layers are formulated as Markov Decision Process (MDP) and solved by two-stage reinforcement learning. Furthermore, to deal with high-density traffic, vehicle selection methods are proposed to reduce the state space and accelerate algorithm convergence without performance degradation. A multi-intersection simulation platform is developed to evaluate the proposed scheme. Simulation results show that the proposed MiVeCC can improve travel efficiency at multiple intersections by up to 4.59 times without collision compared with existing methods.

cs.AI↗

arXiv2020

Are University Rankings Statistically Significant? A Comparison among Chinese Universities and with the USA

Purpose: We address the question of whether differences are statistically significant in the rankings of universities. We propose methods measuring the statistical significance among different universities and illustrate the results by empirical data. Design/methodology/approach: Based on z-testing and overlapping confidence intervals, and using data about 205 Chinese universities included in the Leiden Rankings 2020, we argue that three main groups of Chinese research universities can be distinguished. Findings: When the sample of 205 Chinese universities is merged with the 197 US universities included in Leiden Rankings 2020, the results similarly indicate three main groups: high, middle, low. Using this data (Leiden Rankings and Web-of-Science), the z-scores of the Chinese universities are significantly below those of the US universities albeit with some overlap. Research limitations: We show empirically that differences in ranking may be due to changes in the data, the models, or the modeling effects on the data. The scientometric groupings are not always stable when we use different methods. R&D policy implications: Differences among universities can be tested for their statistical significance. The statistics relativize the values of decimals in the rankings. One can operate with a scheme of low/middle/high in policy debates and leave the more fine-grained rankings of individual universities to operational management and local settings. Originality/value: In the discussion about the rankings of universities, the question of whether differences are statistically significant, is, in our opinion, insufficiently addressed.

cs.DL↗

arXiv2020

Strength of the naked singularity in critical collapse

The critical collapse of a scalar field is a threshold solution of black hole formation, in which a naked singularity arises. We study here the curvature strength of this singularity using a numerical ansatz. The behavior of the Jacobi volume forms is examined along a non-spacelike geodesic in the limit of approach to the singularity. These are seen to be vanishing, thus showing that all physical objects will be crushed to zero size near the singularity. Consequently, although the critical collapse is considered to be a fine-tuning problem, the naked singularity forming is gravitationally strong. This implies that the spacetime cannot be extended beyond the singularity, thus making the singularity genuine and physically interesting. These results imply that the nature of critical collapse may need to be examined and explored further.

gr-qc↗

arXiv2020

Accretion disks around naked singularities

We investigate here the thermal properties of accretion disks in a spacetime for some galactic density profiles in spherical symmetry. The matter distributions have a finite outer radius with a naked central singularity. The luminosities of the accretion disks for some density profile models are found to be higher than those for a Schwarzschild black hole of the same mass. The slopes for the luminosity distributions with respect to frequencies are significantly different, especially at higher frequencies, from that in the Schwarzschild black hole case. Such features may be used to distinguish black holes from naked singularities. The efficiencies for the conversion of the mass energy of the accreting gas into radiation and the strength of naked singularities are analyzed. The novel feature that we find is, the strength of the singularity is different depending on the profiles considered, and the stronger the singularity is, the higher is the efficiency for the accretion disk.

gr-qc↗

arXiv2020

Optimal Machine Intelligence at the Edge of Chaos

It has long been suggested that the biological brain operates at some critical point between two different phases, possibly order and chaos. Despite many indirect empirical evidence from the brain and analytical indication on simple neural networks, the foundation of this hypothesis on generic non-linear systems remains unclear. Here we develop a general theory that reveals the exact edge of chaos is the boundary between the chaotic phase and the (pseudo)periodic phase arising from Neimark-Sacker bifurcation. This edge is analytically determined by the asymptotic Jacobian norm values of the non-linear operator and influenced by the dimensionality of the system. The optimality at the edge of chaos is associated with the highest information transfer between input and output at this point similar to that of the logistic map. As empirical validations, our experiments on the various deep learning models in computer vision demonstrate the optimality of the models near the edge of chaos, and we observe that the state-of-art training algorithms push the models towards such edge as they become more accurate. We further establishes the theoretical understanding of deep learning model generalization through asymptotic stability.

cs.LG↗

arXiv2020

Penalized model-based clustering of fMRI data

Functional magnetic resonance imaging (fMRI) data have become increasingly available and are useful for describing functional connectivity (FC), the relatedness of neuronal activity in regions of the brain. This FC of the brain provides insight into certain neurodegenerative diseases and psychiatric disorders, and thus is of clinical importance. To help inform physicians regarding patient diagnoses, unsupervised clustering of subjects based on FC is desired, allowing the data to inform us of groupings of patients based on shared features of connectivity. Since heterogeneity in FC is present even between patients within the same group, it is important to allow subject-level differences in connectivity, while still pooling information across patients within each group to describe group-level FC. To this end, we propose a random covariance clustering model (RCCM) to concurrently cluster subjects based on their FC networks, estimate the unique FC networks of each subject, and to infer shared network features. Although current methods exist for estimating FC or clustering subjects using fMRI data, our novel contribution is to cluster or group subjects based on similar FC of the brain while simultaneously providing group- and subject-level FC network estimates. The competitive performance of RCCM relative to other methods is demonstrated through simulations in various settings, achieving both improved clustering of subjects and estimation of FC networks. Utility of the proposed method is demonstrated with application to a resting-state fMRI data set collected on 43 healthy controls and 61 participants diagnosed with schizophrenia.

stat.ML↗

arXiv2020

Unified theory to characterize Floquet topological phases by quench dynamics

The conventional characterization of periodically driven systems usually necessitates the time-domain information beyond Floquet bands, hence lacking universal and direct schemes of measuring Floquet topological invariants. Here we propose a unified theory based on quantum quenches to characterize generic $d$-dimensional ($d$D) Floquet topological phases, in which the topological invariants are constructed with only minimal information of the static Floquet bands. For a $d$D phase which is initially static and trivial, we introduce the quench dynamics by suddenly turning on the periodic driving, and show that the quench dynamics exhibits emergent topological patterns in ($d-1$)D momentum subspaces where Floquet bands cross, from which the Floquet topological invariants are directly obtained. This prediction provides a simple and unified characterization, in which one can not only extract the number of conventional and anomalous Floquet boundary modes, but also identify the topologically protected singularities in the phase bands. The applications are illustrated with 1D and 2D models which are readily accessible in cold atom experiments. Our study opens a new framework for the characterization of Floquet topological phases.

cond-mat.quant-gas↗

arXiv2020

Lie transformation on shortcut to adiabaticity in parametric driving quantum system

Shortcut to adiabaticity (STA) is a speed way to produce the same final state that would result in an adiabatic, infinitely slow process. Two typical techniques to engineer STA are developed by either introducing auxiliary counterdiabatic fields or finding new Hamiltonians that own dynamical invariants to constraint the system into the adiabatic paths. In this paper, a consistent method is introduced to naturally connect the above two techniques with a unified Lie algebraic framework, which neatly removes the requirements of finding instantaneous states in the transitionless driving method and the invariant quantities in the invariant-based inverse engineering approach. The general STA schemes for different potential expansions are concisely achieved with the aid of this method.

quant-ph↗

arXiv2020

Ultra-uniform Nanocrystalline Materials via Two-Step Sintering

Nanocrystalline metals and ceramics with <100 nm grain sizes and superior properties (e.g., mechanical strength, hardness, fracture toughness and stored dielectric energy) are of great interest. Much has been discussed about achieving nano grains, but little is known about maintaining grain-size uniformity that is critical for material reliability. An especially intriguing question is whether it is possible to achieve a size distribution narrower than what Hillert[1] theoretically predicted for normal grain growth, a possibility suggested, for growth with a higher growth exponent, by the generalized mean-field theory[2] of Lifshitz, Slyozov, Wagner (LSW)[3,4] and Hillert but never realized in practice. We demonstrate that this can be achieved in bulk materials with an appropriately designed two-step sintering route that (a) takes advantage of the large growth exponent in the intermediate sintering stage to form a most uniform microstructure despite porosity remaining, and (b) freezes the grain growth thereon while continuing densification to reach full density. The resultant dense bulk Al2O3 ceramic has an average grain size of 34 nm and a much narrower size distribution than Hillert's prediction. Bulk Al2O3 with a grain-size distribution narrower than the particle-size distribution of starting powders was also demonstrated using this strategy, as were highly uniform bulk engineering metals and ceramics of either high purity and high melting points (Mo and W-Re) or highly complex compositions (core-shell BaTiO3 and 0.87BaTiO3-0.13Bi(Zn2/3(Nb0.85Ta0.15)1/3)O3).

cond-mat.mtrl-sci↗

arXiv2020

Describing Migdal effects in diamond crystal with atom-centered localized Wannier functions

Recent studies have theoretically investigated the atomic excitation and ionization induced by the dark matter (DM)-nucleus scattering, and it is found that the suddenly recoiled atom is much more likely to excite or lose its electrons than expected. Such phenomenon is called the "Migdal effect". In this paper, we extend the established strategy to describe the Migdal effect in isolated atoms to the case in semiconductors under the framework of tight-binding (TB) approximation. Since the localized aspects of electrons are respected in form of the Wannier functions (WFs), the extension of the existing Migdal approach for isolated atoms is much more natural, while the extensive nature of electrons in solids is reflected in the hopping integrals. We take diamond target as a concrete proof of principle for the methodology, and calculate relevant energy spectra and projected sensitivity of such diamond detector. It turns out that our method as a preliminary attempt is practically effective.

cond-mat.mes-hall↗

arXiv2020

ConvGRU in Fine-grained Pitching Action Recognition for Action Outcome Prediction

Prediction of the action outcome is a new challenge for a robot collaboratively working with humans. With the impressive progress in video action recognition in recent years, fine-grained action recognition from video data turns into a new concern. Fine-grained action recognition detects subtle differences of actions in more specific granularity and is significant in many fields such as human-robot interaction, intelligent traffic management, sports training, health caring. Considering that the different outcomes are closely connected to the subtle differences in actions, fine-grained action recognition is a practical method for action outcome prediction. In this paper, we explore the performance of convolutional gate recurrent unit (ConvGRU) method on a fine-grained action recognition tasks: predicting outcomes of ball-pitching. Based on sequences of RGB images of human actions, the proposed approach achieved the performance of 79.17% accuracy, which exceeds the current state-of-the-art result. We also compared different network implementations and showed the influence of different image sampling methods, different fusion methods and pre-training, etc. Finally, we discussed the advantages and limitations of ConvGRU in such action outcome prediction and fine-grained action recognition tasks.

cs.CV↗

arXiv2020

Mask Detection and Breath Monitoring from Speech: on Data Augmentation, Feature Representation and Modeling

This paper introduces our approaches for the Mask and Breathing Sub-Challenge in the Interspeech COMPARE Challenge 2020. For the mask detection task, we train deep convolutional neural networks with filter-bank energies, gender-aware features, and speaker-aware features. Support Vector Machines follows as the back-end classifiers for binary prediction on the extracted deep embeddings. Several data augmentation schemes are used to increase the quantity of training data and improve our models' robustness, including speed perturbation, SpecAugment, and random erasing. For the speech breath monitoring task, we investigate different bottleneck features based on the Bi-LSTM structure. Experimental results show that our proposed methods outperform the baselines and achieve 0.746 PCC and 78.8% UAR on the Breathing and Mask evaluation set, respectively.

eess.AS↗