Bo Zhang - JILA, National Institute of Standards and Technology and University of Colorado, Boulder, Colorado

Bo Zhang
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Name
Bo Zhang
Affiliation
JILA, National Institute of Standards and Technology and University of Colorado, Boulder, Colorado
City
Boulder
Country
United States

Pubs By Year

Pub Categories

 
Computer Science - Learning (11)
 
Computer Science - Computer Vision and Pattern Recognition (11)
 
Solar and Stellar Astrophysics (8)
 
Astrophysics of Galaxies (6)
 
Physics - Soft Condensed Matter (4)
 
Physics - Accelerator Physics (3)
 
High Energy Astrophysical Phenomena (3)
 
Physics - Optics (2)
 
Mathematics - Optimization and Control (2)
 
Mathematics - Analysis of PDEs (2)
 
Computer Science - Robotics (2)
 
Statistics - Machine Learning (2)
 
Mathematics - Numerical Analysis (2)
 
Computer Science - Computation and Language (1)
 
Physics - Medical Physics (1)
 
Computer Science - Information Retrieval (1)
 
Physics - Materials Science (1)
 
High Energy Physics - Experiment (1)
 
Instrumentation and Methods for Astrophysics (1)
 
Mathematics - Probability (1)
 
Physics - Fluid Dynamics (1)
 
Quantum Physics (1)
 
Quantitative Biology - Biomolecules (1)
 
Physics - Biological Physics (1)
 
Physics - Plasma Physics (1)
 
High Energy Physics - Phenomenology (1)
 
General Relativity and Quantum Cosmology (1)
 
Computer Science - Artificial Intelligence (1)

Publications Authored By Bo Zhang

Effects of nanostructured defects of copper solid surface on the bubble growth in liquid argon have been investigated through a hybrid atomistic-continuum method. The same solid surfaces with five different nanostructures, namely, wedge defect, deep rectangular defect (R-I), shallow rectangular defect (R-II), small rectangular defect (R-III) and no defect, have been modeled at molecular level. The liquid argon is placed on top of the hot solid copper with superheat of 30 K after equilibration is achieved with CFD-MD coupled simulation. Read More

This paper describes a general method for manipulation of nuclear spins in zero magnetic field. In the absence of magnetic fields, the spins lose the individual information on chemical shifts and inequivalent spins can only be distinguished by nuclear gyromagnetic ratios and spin-spin couplings. For spin-1/2 nuclei with different gyromagnetic ratios (i. Read More

Loop closure detection (LCD) is an indispensable part of simultaneous localization and mapping systems (SLAM); it enables robots to produce a consistent map by recognizing previously visited places. When robots operate over extended periods, robustness to viewpoint and condition changes as well as satisfactory real-time performance become essential requirements for a practical LCD system. This paper presents an approach to directly utilize the outputs at the intermediate layer of a pre-trained convolutional neural network (CNN) as image descriptors. Read More

Single-molecule FRET is widely used to study helicases by detecting distance changes between a fluorescent donor and an acceptor anchored to overhangs of a forked DNA duplex. However, it has lacked single-base pair (1-bp) resolution required for revealing stepping dynamics in unwinding because FRET signals are usually blurred by thermal fluctuations of the overhangs. We designed a nanotensioner in which a short DNA is bent to exert a force on the overhangs, just as in optical/magnetic tweezers. Read More

Traditional image clustering methods take a two-step approach, feature learning and clustering, sequentially. However, recent research results demonstrated that combining the separated phases in a unified framework and training them jointly can achieve a better performance. In this paper, we first introduce fully convolutional auto-encoders for image feature learning and then propose a unified clustering framework to learn image representations and cluster centers jointly based on a fully convolutional auto-encoder and soft $k$-means scores. Read More

Interpretability of deep neural networks (DNNs) is essential since it enables users to understand the overall strengths and weaknesses of the models, conveys an understanding of how the models will behave in the future, and how to diagnose and correct potential problems. However, it is challenging to reason about what a DNN actually does due to its opaque or black-box nature. To address this issue, we propose a novel technique to improve the interpretability of DNNs by leveraging the rich semantic information embedded in human descriptions. Read More

Generative adversarial nets (GANs) are good at generating realistic images and have been extended for semi-supervised classification. However, under a two-player formulation, existing work shares competing roles of identifying fake samples and predicting labels via a single discriminator network, which can lead to undesirable incompatibility. We present triple generative adversarial net (Triple-GAN), a flexible game-theoretical framework for classification and class-conditional generation in semi-supervised learning. Read More

Colloidal particles can self-assemble into various ordered structures in fluid flows that have potential applications in biomedicine, materials synthesis and encryption. These dynamic processes are also of fundamental interest for probing the general principles of self-assembly in non-equilibrium conditions. Here, we report a simple microfluidic experiment, where charged colloidal particles self-assemble into flow-aligned 1D strings with regular particle spacing near a solid boundary. Read More

Besides the success on object recognition, machine translation and system control in games, (deep) neural networks have achieved state-of-the-art results in collaborative filtering (CF) recently. Previous neural approaches for CF are either user-based or item-based, which cannot leverage all relevant information explicitly. We propose CF-UIcA, a neural co-autoregressive model for CF tasks, which exploit the structural autoregressiveness in the domains of both users and items. Read More

The $k$-means clustering algorithm is popular but has the following main drawbacks: 1) the number of clusters, $k$, needs to be provided by the user in advance, 2) it can easily reach local minima with randomly selected initial centers, 3) it is sensitive to outliers, and 4) it can only deal with well separated hyperspherical clusters. In this paper, we propose a Local Density Peaks Searching (LDPS) initialization framework to address these issues. The LDPS framework includes two basic components: one of them is the local density that characterizes the density distribution of a data set, and the other is the local distinctiveness index (LDI) which we introduce to characterize how distinctive a data point is compared with its neighbors. Read More

Deep generative models (DGMs) are effective on learning multilayered representations of complex data and performing inference of input data by exploring the generative ability. However, it is relatively insufficient to empower the discriminative ability of DGMs on making accurate predictions. This paper presents max-margin deep generative models (mmDGMs) and a class-conditional variant (mmDCGMs), which explore the strongly discriminative principle of max-margin learning to improve the predictive performance of DGMs in both supervised and semi-supervised learning, while retaining the generative capability. Read More

It is well known that the modulus of the far-field pattern (or phaseless far-field pattern) is invariant under translations of the scattering obstacle if only one plane wave is used as the incident field, so the shape but not the location of the obstacle can be recovered from the phaseless far-field data. In this paper, it is proved that the translation invariance property of the phaseless far-field pattern can be broken if superpositions of two plane waves are used as the incident fields for all wave numbers in a finite interval. Based on this, a recursive Newton-type iteration algorithm in frequencies is then developed to recover both the location and the shape of the obstacle simultaneously from multi-frequency phaseless far-field data. Read More

This paper is concerned with a nonlinear imaging problem, which aims to reconstruct a locally perturbed, perfectly reflecting, infinite plane from intensity-only (or phaseless) far-field or near-field data. A recursive Newton iteration algorithm in frequencies is developed to reconstruct the locally rough surface from multi-frequency intensity-only far-field or near-field data, where the fast integral equation solver developed in [39] is used to solve the direct scattering problem in each iteration. For the case with far-field data, a main feature of our work is that the incident field is taken as a superposition of two plane waves with different directions rather than one plane wave, so the location and shape of the local perturbation of the infinite plane can be reconstructed simultaneously from intensity-only far-field data with multiple wave numbers. Read More

In this paper, we consider the direct and inverse problem of scattering of time-harmonic waves by an unbounded rough interface with a buried impenetrable obstacle. We first study the well-posedness of the direct problem with a local source by the variational method; the well-posedness result is then extended to scattering problems induced by point source waves (PSWs) and hyper-singular point source waves (HSPSWs). For PSW or HSPSW incident waves, the induced total field admits a uniformly bounded estimate in any compact subset far from the source position. Read More

The nature of the spiral structure of the Milky Way has long been debated. Only in the last decade have astronomers been able to accurately measure distances to a substantial number of high-mass star-forming regions, the classic tracers of spiral structure in galaxies. We report distance measurements at radio wavelengths using the Very Long Baseline Array for eight regions of massive star formation near the Local spiral arm of the Milky Way. Read More

Hashing method maps similar data to binary hashcodes with smaller hamming distance, and it has received a broad attention due to its low storage cost and fast retrieval speed. However, the existing limitations make the present algorithms difficult to deal with large-scale datasets: (1) discrete constraints are involved in the learning of the hash function; (2) pairwise or triplet similarity is adopted to generate efficient hashcodes, resulting both time and space complexity are greater than O(n^2). To address these issues, we propose a novel discrete supervised hash learning framework which can be scalable to large-scale datasets. Read More

Motivated by the use of appointment templates in healthcare scheduling practice, we study how to offer appointment slots to patients in order to maximize the utilization of provider time. We develop two models, {\em non-sequential} scheduling and {\em sequential} scheduling, to capture different types of interactions between patients and the scheduling system. The scheduler offers either a single set of appointment slots for the arriving patient to choose from, or multiple sets in sequence, respectively. Read More

We investigated the physical properties of molecular clouds and star formation processes around infrared bubbles which are essentially expanding HII regions. We performed observations of 13 galactic infrared bubble fields containing 18 bubbles. Five molecular lines, 12CO (J=1-0), 13CO (J=1-0), C18O(J=1-0), HCN (J=1-0), and HCO+ (J=1-0), were observed, and several publicly available surveys, GLIMPSE, MIPSGAL, ATLASGAL, BGPS, VGPS, MAGPIS, and NVSS, were used for comparison. Read More

Historically, the weak s-process contribution to metal-poor stars is thought to be extremely small, due to the effect of the secondary-like nature of the neutron source 22Ne(a;n)25Mg in massive stars, which means that metal-poor weak s-process stars could not be found. ET0097 is the first observed carbon-enhanced metal-poor (CEMP) star in the Sculptor dwarf spheroidal galaxy. Because C is enriched and the elements heavier than Ba are not overabundant, ET0097 can be classified as a CEMP-no star. Read More

Consider the wave propagation in a two-layered medium consisting of a homogeneous compressible air or fluid on top of a homogeneous isotropic elastic solid. The interface between the two layers is assumed to be an unbounded rough surface. This paper concerns the time-domain analysis of such an acoustic-elastic interaction problem in an unbounded structure in three dimensions. Read More

Recently significant performance improvement in face detection was made possible by deeply trained convolutional networks. In this report, a novel approach for training state-of-the-art face detector is described. The key is to exploit the idea of hard negative mining and iteratively update the Faster R-CNN based face detector with the hard negatives harvested from a large set of background examples. Read More

One of the cornerstones of modern physics is Einstein's special relativity, with its constant speed of light and zero photon mass assumptions. Constraint on the rest mass m_{\gamma} of photons is a fundamental way to test Einstein's theory, as well as other essential electromagnetic and particle theories. Since non-zero photon mass can give rise to frequency-(or energy-) dependent dispersions, measuring the time delay of photons with different frequencies emitted from explosive astrophysical events is an important and model-independent method to put such a constraint. Read More

We report the first measurement of the odd-isotope fractions for barium, \fodd\, in two extremely metal-poor stars: a CEMP-r/s star \he\ (\feh\,$=-2.42\pm0.11$) and an r-II star \cs\ (\feh\,$=-2. Read More

In this work, we present the new catalog of carbon stars from the LAMOST DR2 catalog. In total, 894 carbon stars are identified from multiple line indices measured from the stellar spectra. Combining the CN bands in the red end with \ctwo\ and other lines, we are able to identify the carbon stars. Read More

We investigate the problem of nonparametrically calibrating the input model in stochastic simulation, given only the availability of output data. While studies on simulation input uncertainty have focused on the use of input data, our inverse model calibration problem arises in various situations where resource and operational limitations hinder the direct observability of input data. We propose a moment-based, maximum entropy framework to infer the input model, by matching statistics between the simulation output and the real- world output. Read More

We introduce ternary weight networks (TWNs) - neural networks with weights constrained to +1, 0 and -1. The Euclidian distance between full (float or double) precision weights and the ternary weights along with a scaling factor is minimized. Besides, a threshold-based ternary function is optimized to get an approximated solution which can be fast and easily computed. Read More

Manifold distances are very effective tools for visual object recognition. However, most of the traditional manifold distances between images are based on the pixel-level comparison and thus easily affected by image rotations and translations. In this paper, we propose a new manifold distance to model the dissimilarities between visual objects based on the Complex Wavelet Structural Similarity (CW-SSIM) index. Read More

The confinement provided by a glass box is proving ideal for the formation of vertically aligned structures and a convenient method for controlling the number of dust particles comprising these dust structures, as well as their size and shape. In this paper, the electronic confinement of the glass box is mapped and the particle interactions between the particle pairs inside the glass box are measured. The ion-wake field is shown to exist within the glass box and its vertical and horizontal extent is measured. Read More

Comparing the parameterized post-Newtonian parameter $\gamma$ values for different types of particles, or the same type of particles with different energies is an important method to test the Einstein Equivalence Principle (EEP). Assuming that the observed time delays are dominated by the gravitational potential of the Laniakea supercluster of galaxies, better results of EEP constraints can be obtained. In this paper, we apply photons from three kinds of cosmic transients, including TeV blazars, gamma-ray bursts as well as fast radio bursts to constrain EEP. Read More

Integration between biology and information science benefits both fields. Many related models have been proposed, such as computational visual cognition models, computational motor control models, integrations of both and so on. In general, the robustness and precision of recognition is one of the key problems for object recognition models. Read More

Memory units have been widely used to enrich the capabilities of deep networks on capturing long-term dependencies in reasoning and prediction tasks, but little investigation exists on deep generative models (DGMs) which are good at inferring high-level invariant representations from unlabeled data. This paper presents a deep generative model with a possibly large external memory and an attention mechanism to capture the local detail information that is often lost in the bottom-up abstraction process in representation learning. By adopting a smooth attention model, the whole network is trained end-to-end by optimizing a variational bound of data likelihood via auto-encoding variational Bayesian methods, where an asymmetric recognition network is learnt jointly to infer high-level invariant representations. Read More

The semiconductor polariton laser promises a new source of coherent light, which, compared to conventional semiconductor photon lasers, has input-energy threshold orders of magnitude lower. However, intensity stability, a defining feature of a coherent state, has remained poor. Intensity noise at many times of the shot-noise of a coherent state has persisted, which has been attributed to multiple mechanisms that are difficult to separate in conventional polariton systems. Read More

Inferior soft-tissue contrast resolution is a major limitation of current CT scanners. The aim of the study is to improve the contrast resolution of CT scanners using dual-energy acquisition. Based on dual-energy material decomposition, the proposed method starts with extracting the outgoing energy spectrum by polychromatic forward projecting the material-selective images. Read More

As one of the most popular classifiers, linear SVMs still have challenges in dealing with very large-scale problems, even though linear or sub-linear algorithms have been developed recently on single machines. Parallel computing methods have been developed for learning large-scale SVMs. However, existing methods rely on solving local sub-optimization problems. Read More

Recent theories predict that when a supercooled liquid approaches the glass transition, particle clusters with a special "amorphous order" nucleate within the liquid, which lead to static correlations dictating the dramatic slowdown of liquid relaxation. The prediction, however, has yet to be verified in 3D experiments. Here, we design a colloidal system, where particles are confined inside spherical cavities with an amorphous layer of particles pinned at the boundary. Read More

Modeling document structure is of great importance for discourse analysis and related applications. The goal of this research is to capture the document intent structure by modeling documents as a mixture of topic words and rhetorical words. While the topics are relatively unchanged through one document, the rhetorical functions of sentences usually change following certain orders in discourse. Read More

We present a discriminative nonparametric latent feature relational model (LFRM) for link prediction to automatically infer the dimensionality of latent features. Under the generic RegBayes (regularized Bayesian inference) framework, we handily incorporate the prediction loss with probabilistic inference of a Bayesian model; set distinct regularization parameters for different types of links to handle the imbalance issue in real networks; and unify the analysis of both the smooth logistic log-loss and the piecewise linear hinge loss. For the nonconjugate posterior inference, we present a simple Gibbs sampler via data augmentation, without making restricting assumptions as done in variational methods. Read More

Methods to solve the relativistic hydrodynamic equations are a key computational kernel in a large number of astrophysics simulations and are crucial to understanding the electromagnetic signals that originate from the merger of astrophysical compact objects. Because of the many physical length scales present when simulating such mergers, these methods must be highly adaptive and capable of automatically resolving numerous localized features and instabilities that emerge throughout the computational domain across many temporal scales. While this has been historically accomplished with adaptive mesh refinement (AMR) based methods, alternatives based on wavelet bases and the wavelet transformation have recently achieved significant success in adaptive representation for advanced engineering applications. Read More

We study the abundances of {\alpha} elements, Fe-peak elements, and neutron-capture elements in Pal 1. We found that the abundances of the SNe Ia and main s-process components of Pal 1 are larger than those of the disk stars and the abundances of the primary component of Pal 1 are smaller than those of the disk stars with similar metallicity. The Fe abundances of Pal 1 and the disk stars mainly originate from the SNe Ia and the primary component, respectively. Read More

Many works have attempted to investigate the astrophysical origin of the neutron-capture elements in the metalpoor star HD 140283. However, no definite conclusions have been drawn. In this work, using the abundancedecomposed approach, we find that the metal-poor star HD 140283 is a weak r-process star. Read More

Recent years have witnessed the prosperity of robots and in order to support consensus and cooperation for multi-robot system, wireless communications and networking among robots and the infrastructure have become indispensable. In this technical note, we first provide an overview of the research contributions on communication-aware motion planning (CAMP) in designing wireless-connected robotic networks (WCRNs), where the degree-of-freedom (DoF) provided by motion and communication capabilities embraced by the robots have not been fully exploited. Therefore, we propose the framework of joint communication-motion planning (JCMP) as well as the architecture for incorporating JCMP in WCRNs. Read More

We estimate the age for the individual stars located at the lower part of the red giant branch from the LAMOST DR2 K giant sample. Taking into account the selection effects and the volume completeness, the age--metallicity map for the stars located between 0.3 and 1. Read More

Field-theoretical method is efficient in predicting the assembling structures of polymeric systems. However, for the polymer/nanoparticle mixture, the continuous density description is not suitable to capture the realistic assembly of particles, especially when the size of particle is much larger than the polymer segment. Here, we developed a field-based model, in which the particles are eventually discrete and hence it can overcome the drawbacks of the conventional field descriptions, e. Read More

The interaction between polymer brush and colloidal particles has been intensively studied in the last two decades. Here we consider a flat chain-grafted substrate immersed in a bath of active particles. Simulations show that an increase in the self-propelling force causes an increase in the number of particles that penetrate into the brush. Read More

In particle accelerators, the build-up of electron cloud may have important influence on beam quality. Especially for the positron and proton accelerators, massive electrons lead to electron cloud, which affects the stability, energy, emittance and beam life adversely. A secondary electron emission (SEE) measurement system has been designed and used to study the SEE of palladium (Pd), TiZrV and TiZrV-Pd with an independently adjustable energy from 50 eV to 5 keV. Read More

Following the discovery of the Higgs boson at LHC, new large colliders are being studied by the international high-energy community to explore Higgs physics in detail and new physics beyond the Standard Model. In China, a two-stage circular collider project CEPC-SPPC is proposed, with the first stage CEPC (Circular Electron Positron Collier, a so-called Higgs factory) focused on Higgs physics, and the second stage SPPC (Super Proton-Proton Collider) focused on new physics beyond the Standard Model. This paper discusses this second stage. Read More

The vacuum chamber of accelerator storage ring need clean ultra-high vacuum environment. TiZrV getter film which was deposited on interior wall of vacuum chamber, can realize distributed pumping, effectively improve the vacuum degree and reduce the longitudinal gradient. But accumulation of pollutants such as N2, O2, will decrease the adsorption ability of non-evaporable getter (NEG), which leads to the reduction of NEG lifetime. Read More

We present doubly stochastic gradient MCMC, a simple and generic method for (approximate) Bayesian inference of deep generative models (DGMs) in a collapsed continuous parameter space. At each MCMC sampling step, the algorithm randomly draws a mini-batch of data samples to estimate the gradient of log-posterior and further estimates the intractable expectation over hidden variables via a neural adaptive importance sampler, where the proposal distribution is parameterized by a deep neural network and learnt jointly. We demonstrate the effectiveness on learning various DGMs in a wide range of tasks, including density estimation, data generation and missing data imputation. Read More

In this work, we provide 2189 photometric- and kinematic-selected member candidates of 24 star clusters from the LAMOST DR2 catalog. We perform two-step membership identification: selection along the stellar track in the color-magnitude diagram, i.e. Read More

In this work, we select the high signal-to-noise ratio spectra of stars from the LAMOST data andmap theirMK classes to the spectral features. The equivalentwidths of the prominent spectral lines, playing the similar role as the multi-color photometry, form a clean stellar locus well ordered by MK classes. The advantage of the stellar locus in line indices is that it gives a natural and continuous classification of stars consistent with either the broadly used MK classes or the stellar astrophysical parameters. Read More