arXiv subjects

Bo Zhao

Publications and source records attributed to Bo Zhao.

At least 253 records · Page 14Linked to original sources

arXiv2021

The young protostellar disk in IRAS16293-2422 B is hot and shows signatures of gravitational instability

Deeply embedded protostars are actively fed from their surrounding envelopes through their protostellar disk. The physical structure of such early disks might be different from that of more evolved sources due to the active accretion. We present 1.3 and 3\,mm ALMA continuum observations at resolutions of 6.5\,au and 12\,au respectively, towards the Class 0 source IRAS 16293-2422 B. The resolved brightness temperatures appear remarkably high, with $T_{\rm b} >$ 100\,K within $\sim$30\,au and $T_{\rm b}$ peak over 400\,K at 3\,mm. Both wavelengths show a lopsided emission with a spectral index reaching values less than 2 in the central $\sim$ 20\,au region. We compare these observations with a series of radiative transfer calculations and synthetic observations of magnetohydrodynamic and radiation hydrodynamic protostellar disk models formed after the collapse of a dense core. Based on our results, we argue that the gas kinematics within the disk may play a more significant role in heating the disk than the protostellar radiation. In particular, our radiation hydrodynamic simulation of disk formation, including heating sources associated with gravitational instabilities, is able to generate the temperatures necessary to explain the high fluxes observed in IRAS 16293B. Besides, the low spectral index values are naturally reproduced by the high optical depth and high inner temperatures of the protostellar disk models. The high temperatures in IRAS 16293B imply that volatile species are mostly in the gas phase, suggesting that a self-gravitating disk could be at the origin of a hot corino.

astro-ph.SR↗

arXiv2021

Violating Kirchhoff's Law of Thermal Radiation in Semitransparent Structures

Kirchhoff's law of thermal radiation imposes a constraint on photon-based energy harvesting processes since part of the incident energy flux is inevitably emitted back to the source. By breaking the reciprocity of the system, it is possible to overcome this restriction and improve the efficiency of energy harvesting. Here, we design and analyze a semitransparent emitter that fully absorbs normally incident energy from a given direction with zero backward and unity forward emissivity. The nearly ideal performance with wavelength-scale thickness is achieved due to the magneto-optical effect and the guided-mode resonance engineered in the emitter structure. We derive the general requirements for the nonreciprocal emitter using the temporal coupled mode theory and the symmetry considerations. Finally, we provide a realistic emitter design based on a photonic crystal slab consisting of a magnetic Weyl semimetal and silicon.

physics.optics↗

arXiv2021

Accelerated MRI Reconstruction with Separable and Enhanced Low-Rank Hankel Regularization

The combination of the sparse sampling and the low-rank structured matrix reconstruction has shown promising performance, enabling a significant reduction of the magnetic resonance imaging data acquisition time. However, the low-rank structured approaches demand considerable memory consumption and are time-consuming due to a noticeable number of matrix operations performed on the huge-size block Hankel-like matrix. In this work, we proposed a novel framework to utilize the low-rank property but meanwhile to achieve faster reconstructions and promising results. The framework allows us to enforce the low-rankness of Hankel matrices constructing from 1D vectors instead of 2D matrices from 1D vectors and thus avoid the construction of huge block Hankel matrix for 2D k-space matrices. Moreover, under this framework, we can easily incorporate other information, such as the smooth phase of the image and the low-rankness in the parameter dimension, to further improve the image quality. We built and validated two models for parallel and parameter magnetic resonance imaging experiments, respectively. Our retrospective in-vivo results indicate that the proposed approaches enable faster reconstructions than the state-of-the-art approaches, e.g., about 8x faster than STDLRSPIRiT, and faithful removal of undersampling artifacts.

eess.IV↗

arXiv2021

Dataset Condensation with Differentiable Siamese Augmentation

In many machine learning problems, large-scale datasets have become the de-facto standard to train state-of-the-art deep networks at the price of heavy computation load. In this paper, we focus on condensing large training sets into significantly smaller synthetic sets which can be used to train deep neural networks from scratch with minimum drop in performance. Inspired from the recent training set synthesis methods, we propose Differentiable Siamese Augmentation that enables effective use of data augmentation to synthesize more informative synthetic images and thus achieves better performance when training networks with augmentations. Experiments on multiple image classification benchmarks demonstrate that the proposed method obtains substantial gains over the state-of-the-art, 7% improvements on CIFAR10 and CIFAR100 datasets. We show with only less than 1% data that our method achieves 99.6%, 94.9%, 88.5%, 71.5% relative performance on MNIST, FashionMNIST, SVHN, CIFAR10 respectively. We also explore the use of our method in continual learning and neural architecture search, and show promising results.

cs.LG↗

arXiv2021

No impact of core-scale magnetic field, turbulence, or velocity gradient on sizes of protostellar disks in Orion A

We compared the sizes and fluxes of a sample of protostellar disks in Orion A measured with the ALMA 0.87 mm continuum data from the VANDAM survey with the physical properties of their ambient environments on the core scale of 0.6 pc estimated with the GBT GAS NH3 and JCMT SCUPOL polarimetric data. We did not find any significant dependence of the disk radii and continuum fluxes on a single parameter on the core scale, such as the non-thermal line width, magnetic field orientation and strength, or magnitude and orientation of the velocity gradient. Among these parameters, we only found a positive correlation between the magnitude of the velocity gradient and the non-thermal line width. Thus, the observed velocity gradients are more likely related to turbulent motion but not large-scale rotation. Our results of no clear dependence of the disk radii on these parameters are more consistent with the expectation from non-ideal MHD simulations of disk formation in collapsing cores, where the disk size is self-regulated by magnetic braking and diffusion, compared to other simulations which only include turbulence and/or a magnetic field misaligned with the rotational axis. Therefore, our results could hint that the non-ideal MHD effects play a more important role in the disk formation. Nevertheless, we cannot exclude the influences on the observed disk size distribution by dynamical interaction in a stellar cluster or amounts of angular momentum on the core scale, which cannot be probed with the current data.

astro-ph.GA↗

arXiv2021

Active Sampling for Accelerated MRI with Low-Rank Tensors

Magnetic resonance imaging (MRI) is a powerful imaging modality that revolutionizes medicine and biology. The imaging speed of high-dimensional MRI is often limited, which constrains its practical utility. Recently, low-rank tensor models have been exploited to enable fast MR imaging with sparse sampling. Most existing methods use some pre-defined sampling design, and active sensing has not been explored for low-rank tensor imaging. In this paper, we introduce an active low-rank tensor model for fast MR imaging. We propose an active sampling method based on a Query-by-Committee model, making use of the benefits of low-rank tensor structure. Numerical experiments on a 3-D MRI data set demonstrate the effectiveness of the proposed method.

cs.CV↗

arXiv2021

Parabolic-accelerating vector waves

Complex vector light fields have become a topic of late due to their exotic features, such as their non--homogeneous transverse polarisation distributions and the non-separable coupling between their spatial and polarisation degrees of freedom. In general, vector beams propagate in free space along straight lines, being the Airy-vector vortex beams the only known exception. Here, we introduce a new family of vector beams that exhibit novel properties that have not been observed before, such as their ability to freely accelerate along parabolic trajectories. In addition, their transverse polarisation distribution only contains polarisation states oriented at exactly the same angle but of different ellipticity. We anticipate that these novel vector beams might not only find applications in fields such as optical manipulation, microscopy or laser material processing, but extend to others.

physics.optics↗

arXiv2021

Evidence for association of triatomic molecule in ultracold $^{23}$Na$^{40}$K and $^{40}$K mixture

Ultracold assembly of diatomic molecules has enabled great advances in controlled chemistry, ultracold chemical physics, and quantum simulation with molecules. Extending the ultracold association to triatomic molecules will offer many new research opportunities and challenges in these fields. A possible approach is to form triatomic molecules in the ultracold atom and diatomic molecule mixture by employing the Feshbach resonance between them. Although the ultracold atom-diatomic-molecule Feshbach resonances have been observed recently, utilizing these resonances to form triatomic molecules remains challenging. Here we report on the evidence of the association of triatomic molecules near the Feshbach resonances between $^{23}$Na$^{40}$K molecules in the rovibrational ground state and $^{40}$K atoms. We apply a radio-frequency pulse to drive the free-bound transition and monitor the loss of $^{23}$Na$^{40}$K molecules. The association of triatomic molecules manifests itself as an additional loss feature in the radio-frequency spectra, which can be distinguished from the atomic loss feature.The binding energy of triatomic molecule is estimated from the measurement. Our work is helpful to understand the complex ultracold atom-molecule Feshbach resonance and may open up an avenue towards the preparation and control of ultracold triatomic molecules.

physics.atom-ph↗

arXiv2021

FedCom: A Byzantine-Robust Local Model Aggregation Rule Using Data Commitment for Federated Learning

Federated learning (FL) is a promising privacy-preserving distributed machine learning methodology that allows multiple clients (i.e., workers) to collaboratively train statistical models without disclosing private training data. Due to the characteristics of data remaining localized and the uninspected on-device training process, there may exist Byzantine workers launching data poisoning and model poisoning attacks, which would seriously deteriorate model performance or prevent the model from convergence. Most of the existing Byzantine-robust FL schemes are either ineffective against several advanced poisoning attacks or need to centralize a public validation dataset, which is intractable in FL. Moreover, to the best of our knowledge, none of the existing Byzantine-robust distributed learning methods could well exert its power in Non-Independent and Identically distributed (Non-IID) data among clients. To address these issues, we propose FedCom, a novel Byzantine-robust federated learning framework by incorporating the idea of commitment from cryptography, which could achieve both data poisoning and model poisoning tolerant FL under practical Non-IID data partitions. Specifically, in FedCom, each client is first required to make a commitment to its local training data distribution. Then, we identify poisoned datasets by comparing the Wasserstein distance among commitments submitted by different clients. Furthermore, we distinguish abnormal local model updates from benign ones by testing each local model's behavior on its corresponding data commitment. We conduct an extensive performance evaluation of FedCom. The results demonstrate its effectiveness and superior performance compared to the state-of-the-art Byzantine-robust schemes in defending against typical data poisoning and model poisoning attacks under practical Non-IID data distributions.

cs.LG↗

arXiv2021

One-bit Spectrum Sensing with the Eigenvalue Moment Ratio Approach

One-bit analog-to-digital converter (ADC), performing signal sampling as an extreme simple comparator, is an overwhelming technology for spectrum sensing due to its low-cost, low-power consumptions and high sampling rate. In this letter, we propose a novel one-bit sensing approach based on the eigenvalue moment ratio (EMR), which has been proved to be highly efficient for conventional multi-antenna spectrum sensing in $\infty$-bit situation. Particularly, we determine the asymptotic distribution of one-bit EMR under null hypothesis via the central limited theorem (CLT), allowing us to perform spectrum sensing with one-bit samples directly. Theoretical and simulation analysis show the new approach can provide reasonably good sensing performance at a low hardware cost.

cs.IT↗

arXiv2021

High-Performance Photonic Transformers for DC Voltage Conversion

Direct current (DC) converters play an essential role in electronic circuits. Conventional high-efficiency DC voltage converters, especially step-up type, rely on switch-mode operation, where energy is periodically stored within and released from inductors and/or capacitors connected in a variety of circuit topologies. However, since these energy storage components, especially inductors, are difficult to scale down, miniaturization of switching converters for on-chip or in-package electronics faces fundamental challenges. Furthermore, the resulting switching currents produce electromagnetic noise, which can cause interference problems in nearby circuits, and even acoustic noise and mechanical vibrations that deteriorate the environment. In order to overcome the limitations of switch-mode converters, photonic transformers, where voltage conversion is achieved through the use of light emission and detection processes, have been demonstrated. However, the demonstrated efficiency is significantly below that of the switch-mode converter. Here we perform a theoretical analysis based on detailed balance, which shows that with a monolithically integrated design that enables efficient photon transport, the photonic transformer can operate with a near-unity conversion efficiency and high voltage conversion ratio. We validate the theoretical analysis with an experiment on a transformer constructed with off-the-shelf discrete components. Our experiment showcases near noiseless operation, as well as a voltage conversion ratio that is significantly higher than obtained in previous photonic transformer works. Our finding points to a high-performance optical solution to miniaturizing DC power converters for electronics and improving the electromagnetic compatibility and quality of electrical power.

physics.optics↗

arXiv2021

Concentric Spherical GNN for 3D Representation Learning

Learning 3D representations that generalize well to arbitrarily oriented inputs is a challenge of practical importance in applications varying from computer vision to physics and chemistry. We propose a novel multi-resolution convolutional architecture for learning over concentric spherical feature maps, of which the single sphere representation is a special case. Our hierarchical architecture is based on alternatively learning to incorporate both intra-sphere and inter-sphere information. We show the applicability of our method for two different types of 3D inputs, mesh objects, which can be regularly sampled, and point clouds, which are irregularly distributed. We also propose an efficient mapping of point clouds to concentric spherical images, thereby bridging spherical convolutions on grids with general point clouds. We demonstrate the effectiveness of our approach in improving state-of-the-art performance on 3D classification tasks with rotated data.

cs.LG↗

arXiv2021

Interpretable Attention Guided Network for Fine-grained Visual Classification

Fine-grained visual classification (FGVC) is challenging but more critical than traditional classification tasks. It requires distinguishing different subcategories with the inherently subtle intra-class object variations. Previous works focus on enhancing the feature representation ability using multiple granularities and discriminative regions based on the attention strategy or bounding boxes. However, these methods highly rely on deep neural networks which lack interpretability. We propose an Interpretable Attention Guided Network (IAGN) for fine-grained visual classification. The contributions of our method include: i) an attention guided framework which can guide the network to extract discriminitive regions in an interpretable way; ii) a progressive training mechanism obtained to distill knowledge stage by stage to fuse features of various granularities; iii) the first interpretable FGVC method with a competitive performance on several standard FGVC benchmark datasets.

cs.CV↗

arXiv2021

Dataset Condensation with Gradient Matching

As the state-of-the-art machine learning methods in many fields rely on larger datasets, storing datasets and training models on them become significantly more expensive. This paper proposes a training set synthesis technique for data-efficient learning, called Dataset Condensation, that learns to condense large dataset into a small set of informative synthetic samples for training deep neural networks from scratch. We formulate this goal as a gradient matching problem between the gradients of deep neural network weights that are trained on the original and our synthetic data. We rigorously evaluate its performance in several computer vision benchmarks and demonstrate that it significantly outperforms the state-of-the-art methods. Finally we explore the use of our method in continual learning and neural architecture search and report promising gains when limited memory and computations are available.

cs.CV↗

arXiv2021

FAUST II. Discovery of a Secondary Outflow in IRAS 15398-3359: Variability in Outflow Direction during the Earliest Stage of Star Formation?

We have observed the very low-mass Class 0 protostar IRAS 15398-3359 at scales ranging from 50 au to 1800 au, as part of the ALMA Large Program FAUST. We uncover a linear feature, visible in H2CO, SO, and C18O line emission, which extends from the source along a direction almost perpendicular to the known active outflow. Molecular line emission from H2CO, SO, SiO, and CH3OH further reveals an arc-like structure connected to the outer end of the linear feature and separated from the protostar, IRAS 15398-3359, by 1200 au. The arc-like structure is blue-shifted with respect to the systemic velocity. A velocity gradient of 1.2 km/s over 1200 au along the linear feature seen in the H2CO emission connects the protostar and the arc-like structure kinematically. SO, SiO, and CH3OH are known to trace shocks, and we interpret the arc-like structure as a relic shock region produced by an outflow previously launched by IRAS 15398-3359. The velocity gradient along the linear structure can be explained as relic outflow motion. The origins of the newly observed arc-like structure and extended linear feature are discussed in relation to turbulent motions within the protostellar core and episodic accretion events during the earliest stage of protostellar evolution.

astro-ph.SR↗

arXiv2021

Quantum Adiabatic Doping for Atomic Fermi-Hubbard Quantum Simulations

There have been considerable research efforts devoted to quantum simulations of Fermi-Hubbard model with ultracold atoms loaded in optical lattices. In such experiments, the antiferromagnetically ordered quantum state has been achieved at half filling in recent years. The atomic lattice away from half filling is expected to host d-wave superconductivity, but its low temperature phases have not been reached. In a recent work, we proposed an approach of incommensurate quantum adiabatic doping, using quantum adiabatic evolution of an incommensurate lattice for preparation of the highly correlated many-body ground state of the doped Fermi-Hubbard model starting from a unit-filling band insulator. Its feasibility has been demonstrated with numerical simulations of the adiabatic preparation for certain incommensurate particle-doping fractions, where the major problem to circumvent is the atomic localization in the incommensurate lattice. Here we carry out a systematic study of the quantum adiabatic doping for a wide range of doping fractions from particle-doping to hole-doping, including both commensurate and incommensurate cases. We find that there is still a localization-like slowing-down problem at commensurate fillings, and that it becomes less harmful in the hole-doped regime. With interactions, the adiabatic preparation is found to be more efficient for that interaction effect destabilizes localization. For both free and interacting cases, we find the adiabatic doping has better performance in the hole-doped regime than the particle-doped regime. We also study adiabatic doping starting from the half-filling Mott insulator, which is found to be more efficient for certain filling fractions.

cond-mat.quant-gas↗

arXiv2020

Multiple Plans are Better than One: Diverse Stochastic Planning

In planning problems, it is often challenging to fully model the desired specifications. In particular, in human-robot interaction, such difficulty may arise due to human's preferences that are either private or complex to model. Consequently, the resulting objective function can only partially capture the specifications and optimizing that may lead to poor performance with respect to the true specifications. Motivated by this challenge, we formulate a problem, called diverse stochastic planning, that aims to generate a set of representative -- small and diverse -- behaviors that are near-optimal with respect to the known objective. In particular, the problem aims to compute a set of diverse and near-optimal policies for systems modeled by a Markov decision process. We cast the problem as a constrained nonlinear optimization for which we propose a solution relying on the Frank-Wolfe method. We then prove that the proposed solution converges to a stationary point and demonstrate its efficacy in several planning problems.

cs.RO↗

arXiv2020

Privacy-Preserving Technology to Help Millions of People: Federated Prediction Model for Stroke Prevention

Prevention of stroke with its associated risk factors has been one of the public health priorities worldwide. Emerging artificial intelligence technology is being increasingly adopted to predict stroke. Because of privacy concerns, patient data are stored in distributed electronic health record (EHR) databases, voluminous clinical datasets, which prevent patient data from being aggregated and restrains AI technology to boost the accuracy of stroke prediction with centralized training data. In this work, our scientists and engineers propose a privacy-preserving scheme to predict the risk of stroke and deploy our federated prediction model on cloud servers. Our system of federated prediction model asynchronously supports any number of client connections and arbitrary local gradient iterations in each communication round. It adopts federated averaging during the model training process, without patient data being taken out of the hospitals during the whole process of model training and forecasting. With the privacy-preserving mechanism, our federated prediction model trains over all the healthcare data from hospitals in a certain city without actual data sharing among them. Therefore, it is not only secure but also more accurate than any single prediction model that trains over the data only from one single hospital. Especially for small hospitals with few confirmed stroke cases, our federated model boosts model performance by 10%~20% in several machine learning metrics. To help stroke experts comprehend the advantage of our prediction system more intuitively, we developed a mobile app that collects the key information of patients' statistics and demonstrates performance comparisons between the federated prediction model and the single prediction model during the federated training process.

cs.LG↗