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Researcher profile

Bo Zhao

Bo Zhao contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Computer Visionastro-ph.SRphysics.opticsastro-ph.GAMachine Learningastro-ph.EPphysics.app-phArtificial Intelligencecond-mat.mes-hallcond-mat.mtrl-sciCryptography and Securityphysics.atom-phastro-ph.HEcond-mat.quant-gaseess.SYInformation RetrievalRoboticsSocial and Information NetworksSystems and Control

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Published work

41 published item(s)

preprint2026arXiv

Focusable Monocular Depth Estimation

Monocular depth foundation models generalize well across scenes, yet they are typically optimized with uniform pixel-wise objectives that do not distinguish user-specified or task-relevant target regions from the surrounding context. We therefore introduce Focusable Monocular Depth Estimation (FDE), a region-aware depth estimation task in which, given a specified target region, the model is required to prioritize foreground depth accuracy, preserve sharp boundary transitions, and maintain coherent global scene geometry. To prioritize task-critical region modeling, we propose FocusDepth, a prompt-conditioned monocular relative depth estimation framework that guides depth modeling to focus on target regions via box/text prompts. The core Multi-Scale Spatial-Aligned Fusion (MSSA) in FocusDepth spatially aligns multi-scale features from Segment Anything Model 3 to the Depth Anything family and injects them through scale-specific, gated conditional fusion. This enables dense prompt cue injection without disrupting geometric representations, thereby endowing the depth estimation model with focused perception capability. To study FDE, we establish FDE-Bench, a target-centric monocular relative depth benchmark built from image-target-depth triplets across five datasets, containing 252.9K/72.5K train/val triplets and 972 categories spanning real-world and embodied simulation environments. On FDE-Bench, FocusDepth consistently improves over globally fine-tuned DA2/DA3 baselines under both box and text prompts, with the largest gains appearing in target boundary and foreground regions while preserving global scene geometry. Ablations show that MSSA's spatial alignment is the key design factor, as disrupting prompt-geometry correspondence increases AbsRel by up to 13.8%.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Symmetry Teleportation for Accelerated Optimization

Existing gradient-based optimization methods update parameters locally, in a direction that minimizes the loss function. We study a different approach, symmetry teleportation, that allows parameters to travel a large distance on the loss level set, in order to improve the convergence speed in subsequent steps. Teleportation exploits symmetries in the loss landscape of optimization problems. We derive loss-invariant group actions for test functions in optimization and multi-layer neural networks, and prove a necessary condition for teleportation to improve convergence rate. We also show that our algorithm is closely related to second order methods. Experimentally, we show that teleportation improves the convergence speed of gradient descent and AdaGrad for several optimization problems including test functions, multi-layer regressions, and MNIST classification.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

CAFE: Learning to Condense Dataset by Aligning Features

Dataset condensation aims at reducing the network training effort through condensing a cumbersome training set into a compact synthetic one. State-of-the-art approaches largely rely on learning the synthetic data by matching the gradients between the real and synthetic data batches. Despite the intuitive motivation and promising results, such gradient-based methods, by nature, easily overfit to a biased set of samples that produce dominant gradients, and thus lack global supervision of data distribution. In this paper, we propose a novel scheme to Condense dataset by Aligning FEatures (CAFE), which explicitly attempts to preserve the real-feature distribution as well as the discriminant power of the resulting synthetic set, lending itself to strong generalization capability to various architectures. At the heart of our approach is an effective strategy to align features from the real and synthetic data across various scales, while accounting for the classification of real samples. Our scheme is further backed up by a novel dynamic bi-level optimization, which adaptively adjusts parameter updates to prevent over-/under-fitting. We validate the proposed CAFE across various datasets, and demonstrate that it generally outperforms the state of the art: on the SVHN dataset, for example, the performance gain is up to 11%. Extensive experiments and analyses verify the effectiveness and necessity of proposed designs.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

DeepCore: A Comprehensive Library for Coreset Selection in Deep Learning

Coreset selection, which aims to select a subset of the most informative training samples, is a long-standing learning problem that can benefit many downstream tasks such as data-efficient learning, continual learning, neural architecture search, active learning, etc. However, many existing coreset selection methods are not designed for deep learning, which may have high complexity and poor generalization performance. In addition, the recently proposed methods are evaluated on models, datasets, and settings of different complexities. To advance the research of coreset selection in deep learning, we contribute a comprehensive code library, namely DeepCore, and provide an empirical study on popular coreset selection methods on CIFAR10 and ImageNet datasets. Extensive experiments on CIFAR10 and ImageNet datasets verify that, although various methods have advantages in certain experiment settings, random selection is still a strong baseline.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

FaceMAE: Privacy-Preserving Face Recognition via Masked Autoencoders

Face recognition, as one of the most successful applications in artificial intelligence, has been widely used in security, administration, advertising, and healthcare. However, the privacy issues of public face datasets have attracted increasing attention in recent years. Previous works simply mask most areas of faces or synthesize samples using generative models to construct privacy-preserving face datasets, which overlooks the trade-off between privacy protection and data utility. In this paper, we propose a novel framework FaceMAE, where the face privacy and recognition performance are considered simultaneously. Firstly, randomly masked face images are used to train the reconstruction module in FaceMAE. We tailor the instance relation matching (IRM) module to minimize the distribution gap between real faces and FaceMAE reconstructed ones. During the deployment phase, we use trained FaceMAE to reconstruct images from masked faces of unseen identities without extra training. The risk of privacy leakage is measured based on face retrieval between reconstructed and original datasets. Experiments prove that the identities of reconstructed images are difficult to be retrieved. We also perform sufficient privacy-preserving face recognition on several public face datasets (i.e. CASIA-WebFace and WebFace260M). Compared to previous state of the arts, FaceMAE consistently \textbf{reduces at least 50\% error rate} on LFW, CFP-FP and AgeDB.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

FAUST III. Misaligned rotations of the envelope, outflow, and disks in the multiple protostellar system of VLA 1623$-$2417

We report a study of the low-mass Class-0 multiple system VLA 1623AB in the Ophiuchus star-forming region, using H$^{13}$CO$^+$ ($J=3-2$), CS ($J=5-4$), and CCH ($N=3-2$) lines as part of the ALMA Large Program FAUST. The analysis of the velocity fields revealed the rotation motion in the envelope and the velocity gradients in the outflows (about 2000 au down to 50 au). We further investigated the rotation of the circum-binary VLA 1623A disk as well as the VLA 1623B disk. We found that the minor axis of the circum-binary disk of VLA 1623A is misaligned by about 12 degrees with respect to the large-scale outflow and the rotation axis of the envelope. In contrast, the minor axis of the circum-binary disk is parallel to the large-scale magnetic field according to previous dust polarization observations, suggesting that the misalignment may be caused by the different directions of the envelope rotation and the magnetic field. If the velocity gradient of the outflow is caused by rotation, the outflow has a constant angular momentum and the launching radius is estimated to be $5-16$ au, although it cannot be ruled out that the velocity gradient is driven by entrainments of the two high-velocity outflows. Furthermore, we detected for the first time a velocity gradient associated with rotation toward the VLA 16293B disk. The velocity gradient is opposite to the one from the large-scale envelope, outflow, and circum-binary disk. The origin of its opposite gradient is also discussed.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Generation of arbitrary abruptly autofusing circular Airy Gaussian vortex vector modes

Complex vector modes represent a general state of light nonseparable in their spatial and polarization degrees of freedom, which have inspired a wide variety of novel applications and phenomena, such as their unexpected propagation behaviour. For example, they can propagate describing periodic polarization transitions, changing from one vector beam to another. Here, we put forward a novel class of vector modes with the capability to experience an abruptly autofocusing behaviour. To achieve such beams, we encode the spatial degree of freedom in the Circular Airy Gaussian vortex (CAGV) beams. We demonstrate the experimental generation of arbitrary CAGV vector beams and evince some of their properties, such as a rotation of intermodal phase. We anticipate that the fascinating properties of theses modes will prompt the development of novel applications associated to their autofocusing behaviour and polarization distribution.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Increasing mass-to-flux ratio from the dense core to the protostellar envelope around the Class 0 protostar HH 211

To study transportation of magnetic flux from large to small scales in protostellar sources, we analyzed the Nobeyama 45-m N2H+ (1-0), JCMT 850 um polarization, and ALMA C18O (2-1) and 1.3 mm and 0.8 mm (polarized) continuum data of the Class 0 protostar HH 211. The magnetic field strength in the dense core on a 0.1 pc scale was estimated with the single-dish line and polarization data using the Davis-Chandrasekhar-Fermi method, and that in the protostellar envelope on a 600 au scale was estimated from the force balance between the gravity and magnetic field tension by analyzing the gas kinematics and magnetic field structures with the ALMA data. Our analysis suggests that from 0.1 pc to 600 au scales, the magnetic field strength increases from 40-107 uG to 0.3-1.2 mG with a scaling relation between the magnetic field strength and density of $B \propto ρ^{0.36\pm0.08}$, and the mass-to-flux ratio increases from 1.2-3.7 to 9.1-32.3. The increase in the mass-to-flux ratio could suggest that the magnetic field is partially decoupled from the neutral matter between 0.1 pc and 600 au scales, and hint at efficient ambipolar diffusion in the infalling protostellar envelope in HH 211, which is the dominant non-ideal magnetohydrodynamic effect considering the density on these scales. Thus, our results could support the scenario of efficient ambipolar diffusion enabling the formation of the 20 au Keplerian disk in HH 211.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

LIMO: Latent Inceptionism for Targeted Molecule Generation

Generation of drug-like molecules with high binding affinity to target proteins remains a difficult and resource-intensive task in drug discovery. Existing approaches primarily employ reinforcement learning, Markov sampling, or deep generative models guided by Gaussian processes, which can be prohibitively slow when generating molecules with high binding affinity calculated by computationally-expensive physics-based methods. We present Latent Inceptionism on Molecules (LIMO), which significantly accelerates molecule generation with an inceptionism-like technique. LIMO employs a variational autoencoder-generated latent space and property prediction by two neural networks in sequence to enable faster gradient-based reverse-optimization of molecular properties. Comprehensive experiments show that LIMO performs competitively on benchmark tasks and markedly outperforms state-of-the-art techniques on the novel task of generating drug-like compounds with high binding affinity, reaching nanomolar range against two protein targets. We corroborate these docking-based results with more accurate molecular dynamics-based calculations of absolute binding free energy and show that one of our generated drug-like compounds has a predicted $K_D$ (a measure of binding affinity) of $6 \cdot 10^{-14}$ M against the human estrogen receptor, well beyond the affinities of typical early-stage drug candidates and most FDA-approved drugs to their respective targets. Code is available at https://github.com/Rose-STL-Lab/LIMO.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Nonreciprocal Infrared Absorption via Resonant Magneto-optical Coupling to InAs

Nonreciprocal elements are a vital building block of electrical and optical systems. In the infrared regime, there is a particular interest in structures that break reciprocity because their thermal absorptive (and emissive) properties should not obey the Kirchhoff thermal radiation law. In this work, we break time-reversal symmetry and reciprocity in n-type doped magneto-optic InAs with a static magnetic field where light coupling is mediated by a guided-mode-resonator (GMR) structure whose resonant frequency coincides with the epsilon-near-zero (ENZ) resonance of the doped InAs. Using this structure, we observe the nonreciprocal absorptive behavior as a function of magnetic field and scattering angle in the infrared. Accounting for resonant and nonresonant optical scattering, we reliably model experimental results that break reciprocal absorption relations in the infrared. The ability to design such nonreciprocal absorbers opens an avenue to explore devices with unequal absorptivity and emissivity in specific channels.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

One Password: An Encryption Scheme for Hiding Users' Register Information

In recent years, the attack which leverages register information (e.g. accounts and passwords) leaked from 3rd party applications to try other applications is popular and serious. We call this attack "database collision". Traditionally, people have to keep dozens of accounts and passwords for different applications to prevent this attack. In this paper, we propose a novel encryption scheme for hiding users' register information and preventing this attack. Specifically, we first hash the register information using existing safe hash function. Then the hash string is hidden, instead a coefficient vector is stored for verification. Coefficient vectors of the same register information are generated randomly for different applications. Hence, the original information is hardly cracked by dictionary based attack or database collision in practice. Using our encryption scheme, each user only needs to keep one password for dozens of applications.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Privacy for Free: How does Dataset Condensation Help Privacy?

To prevent unintentional data leakage, research community has resorted to data generators that can produce differentially private data for model training. However, for the sake of the data privacy, existing solutions suffer from either expensive training cost or poor generalization performance. Therefore, we raise the question whether training efficiency and privacy can be achieved simultaneously. In this work, we for the first time identify that dataset condensation (DC) which is originally designed for improving training efficiency is also a better solution to replace the traditional data generators for private data generation, thus providing privacy for free. To demonstrate the privacy benefit of DC, we build a connection between DC and differential privacy, and theoretically prove on linear feature extractors (and then extended to non-linear feature extractors) that the existence of one sample has limited impact ($O(m/n)$) on the parameter distribution of networks trained on $m$ samples synthesized from $n (n \gg m)$ raw samples by DC. We also empirically validate the visual privacy and membership privacy of DC-synthesized data by launching both the loss-based and the state-of-the-art likelihood-based membership inference attacks. We envision this work as a milestone for data-efficient and privacy-preserving machine learning.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Resonant control of elastic collisions between $^{23}$Na$^{40}$K molecules and $^{40}$K atoms

We have demonstrated the resonant control of the elastic scattering cross sections in the vicinity of Feshbach resonances between $^{23}$Na$^{40}$K molecules and $^{40}$K atoms by studying the thermalization between them. The elastic scattering cross sections vary by more than two orders of magnitude close to the resonance, and can be well described by an asymmetric Fano profile. The parameters that characterize the magnetically tunable s-wave scattering length are determined from the elastic scattering cross sections. The observation of resonantly controlled elastic scattering cross sections opens up the possibility to study strongly interacting atom-molecule mixtures and improve our understanding of the complex atom-molecule Feshbach resonances.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

The Central 1000 au of a Pre-stellar Core Revealed with ALMA. II. Almost Complete Freeze-out

Pre-stellar cores represent the initial conditions in the process of star and planet formation. Their low temperatures ($<$10 K) allow the formation of thick icy dust mantles, which will be partially preserved in the future protoplanetary disks, ultimately affecting the chemical composition of planetary systems. Previous observations have shown that carbon- and oxygen-bearing species, in particular CO, are heavily depleted in pre-stellar cores due to the efficient molecular freeze-out onto the surface of cold dust grains. However, N-bearing species such as NH$_3$ and, in particular, its deuterated isotopologues, appear to maintain high abundances where CO molecules are mainly in solid phase. Thanks to ALMA, we present here the first clear observational evidence of NH$_2$D freeze-out toward the L1544 pre-stellar core, suggestive of the presence of a&#34;complete-depletion zone&#34; within a $\simeq$1800 au radius, in agreement with astrochemical pre-stellar core model predictions. Our state-of-the-art chemical model coupled with a non-LTE radiative transfer code demonstrates that NH$_2$D becomes mainly incorporated in icy mantles in the central 2000 au and starts freezing-out already at $\simeq$7000 au. Radiative transfer effects within the pre-stellar core cause the NH$_2$D(1$_{11}$-1$_{01}$) emission to appear centrally concentrated, with a flattened distribution within the central $\simeq$3000 au, unlike the 1.3 mm dust continuum emission which shows a clear peak within the central $\simeq$1800 au. This prevented NH$_2$D freeze-out to be detected in previous observations, where the central 1000 au cannot be spatially resolved.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Towards Understanding and Harnessing the Effect of Image Transformation in Adversarial Detection

Deep neural networks (DNNs) are threatened by adversarial examples. Adversarial detection, which distinguishes adversarial images from benign images, is fundamental for robust DNN-based services. Image transformation is one of the most effective approaches to detect adversarial examples. During the last few years, a variety of image transformations have been studied and discussed to design reliable adversarial detectors. In this paper, we systematically synthesize the recent progress on adversarial detection via image transformations with a novel classification method. Then, we conduct extensive experiments to test the detection performance of image transformations against state-of-the-art adversarial attacks. Furthermore, we reveal that each individual transformation is not capable of detecting adversarial examples in a robust way, and propose a DNN-based approach referred to as \emph{AdvJudge}, which combines scores of 9 image transformations. Without knowing which individual scores are misleading or not misleading, AdvJudge can make the right judgment, and achieve a significant improvement in detection rate. Finally, we utilize an explainable AI tool to show the contribution of each image transformation to adversarial detection. Experimental results show that the contribution of image transformations to adversarial detection is significantly different, the combination of them can significantly improve the generic detection ability against state-of-the-art adversarial attacks.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Tunable Multi-Peak Perfect Absorbers Based on Borophene for High-Performance Near-Infrared Refractive Index Sensing

Borophene has recently attracted significant attention as an emerging two-dimensional monoelemental material for refractive index sensing because of its ultra-high surface-to-volume ratio and outstanding surface sensitivity. However, current research mainly focuses on designing borophene-based sensors with single-peak absorption, which lacks reliability compared to the performance of multi-peak sensors. In this paper, high-performance borophene refractive index sensors with multiple nearly perfect absorption peaks are proposed. The geometric parameters of the metadevices are optimized using finite-difference time-domain (FDTD) simulations to obtain three strong absorption peaks in the near-infrared (near-IR) regime with intensities of 99.72%, 99.18%, and 98.13%. Further calculations show that the sensitivities of the three strong peaks reach 339.1 nm shift per refractive index units (nm/RIU), 410.1 nm/RIU, and 738.1 nm/RIU, and their corresponding figure of merits (FOMs) reach up to 15.41/RIU, 14.65/RIU and 10.85/RIU, respectively, exhibiting great potential for near-IR refractive index sensing. Moreover, the three absorption peaks can be easily tuned by adjusting the carrier density of borophene, which can be realized by applying different external electric bias voltages. These results can provide theoretical guidance for the development of multi-peak near-IR dynamic integrated photonic devices

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Accretion Flows or Outflow Cavities? Uncovering the Gas Dynamics around Lupus 3-MMS

Understanding how material accretes onto the rotationally supported disk from the surrounding envelope of gas and dust in the youngest protostellar systems is important for describing how disks are formed. Magnetohydrodynamic simulations of magnetized, turbulent disk formation usually show spiral-like streams of material (accretion flows) connecting the envelope to the disk. However, accretion flows in these early stages of protostellar formation still remain poorly characterized due to their low intensity and possibly some extended structures are disregarded as being part of the outflow cavity. We use ALMA archival data of a young Class 0 protostar, Lupus 3-MMS, to uncover four extended accretion flow-like structures in C$^{18}$O that follow the edges of the outflows. We make various types of position-velocity cuts to compare with the outflows and find the extended structures are not consistent with the outflow emission, but rather more consistent with a simple infall model. We then use a dendrogram algorithm to isolate five sub-structures in position-position-velocity space. Four out of the five sub-structures fit well ($>$95%) with our simple infall model, with specific angular momenta between $2.7-6.9\times10^{-4}\,$km$\,$s$^{-1}\,$pc and mass-infall rates of $0.5-1.1\times10^{-6}\,M_{\odot}\,$yr$^{-1}$. Better characterization of the physical structure in the supposed &#34;outflow-cavities&#34; is important to disentangle the true outflow cavities and accretion flows.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

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.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A protostellar system fed by a streamer of 10,500 au length

Binary formation is an important aspect of star formation. One possible route for close-in binary formation is disk fragmentation$^{[1,2,3]}$. Recent observations show small scale asymmetries (<300 au) around young protostars$^{[2,4]}$, although not always resolving the circumbinary disk, are linked to disk phenomena$^{[5,6]}$. In later stages, resolved circumbinary disk observations$^{[7]}$ (<200 au) show similar asymmetries, suggesting the origin of the asymmetries arises from binary-disk interactions$^{[8,9,10]}$. We observed one of the youngest systems to study the connection between disk and dense core. We find for the first time a bright and clear streamer in chemically fresh material (Carbon-chain species) that originates from outside the dense core (>10,500 au). This material connects the outer dense core with the region where asymmetries arise near disk scales. This new structure type, 10x larger than those seen near disk scales, suggests a different interpretation of previous observations: large-scale accretion flows funnel material down to disk scales. These results reveal the under-appreciated importance of the local environment on the formation and evolution of disks in early systems$^{[13,14]}$ and a possible initial condition for the formation of annular features in young disks$^{[15,16]}$.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Attribute-guided image generation from layout

Recent approaches have achieved great success in image generation from structured inputs, e.g., semantic segmentation, scene graph or layout. Although these methods allow specification of objects and their locations at image-level, they lack the fidelity and semantic control to specify visual appearance of these objects at an instance-level. To address this limitation, we propose a new image generation method that enables instance-level attribute control. Specifically, the input to our attribute-guided generative model is a tuple that contains: (1) object bounding boxes, (2) object categories and (3) an (optional) set of attributes for each object. The output is a generated image where the requested objects are in the desired locations and have prescribed attributes. Several losses work collaboratively to encourage accurate, consistent and diverse image generation. Experiments on Visual Genome dataset demonstrate our model&#39;s capacity to control object-level attributes in generated images, and validate plausibility of disentangled object-attribute representation in the image generation from layout task. Also, the generated images from our model have higher resolution, object classification accuracy and consistency, as compared to the previous state-of-the-art.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Augmented Bi-path Network for Few-shot Learning

Few-shot Learning (FSL) which aims to learn from few labeled training data is becoming a popular research topic, due to the expensive labeling cost in many real-world applications. One kind of successful FSL method learns to compare the testing (query) image and training (support) image by simply concatenating the features of two images and feeding it into the neural network. However, with few labeled data in each class, the neural network has difficulty in learning or comparing the local features of two images. Such simple image-level comparison may cause serious mis-classification. To solve this problem, we propose Augmented Bi-path Network (ABNet) for learning to compare both global and local features on multi-scales. Specifically, the salient patches are extracted and embedded as the local features for every image. Then, the model learns to augment the features for better robustness. Finally, the model learns to compare global and local features separately, i.e., in two paths, before merging the similarities. Extensive experiments show that the proposed ABNet outperforms the state-of-the-art methods. Both quantitative and visual ablation studies are provided to verify that the proposed modules lead to more precise comparison results.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Continual Representation Learning for Biometric Identification

With the explosion of digital data in recent years, continuously learning new tasks from a stream of data without forgetting previously acquired knowledge has become increasingly important. In this paper, we propose a new continual learning (CL) setting, namely ``continual representation learning&#39;&#39;, which focuses on learning better representation in a continuous way. We also provide two large-scale multi-step benchmarks for biometric identification, where the visual appearance of different classes are highly relevant. In contrast to requiring the model to recognize more learned classes, we aim to learn feature representation that can be better generalized to not only previously unseen images but also unseen classes/identities. For the new setting, we propose a novel approach that performs the knowledge distillation over a large number of identities by applying the neighbourhood selection and consistency relaxation strategies to improve scalability and flexibility of the continual learning model. We demonstrate that existing CL methods can improve the representation in the new setting, and our method achieves better results than the competitors.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Controlling the dopant profile for SRH suppression at low current densities in $λ\approx$ 1330nm GaInAsP light-emitting diodes

The quantum efficiency of double hetero-junction light-emitting diodes (LEDs) can be significantly enhanced at low current density by tailoring the spatial profile of dopants to suppress Shockley-Read-Hall (SRH) recombination. To demonstrate this effect we model, design, grow, fabricate, and test a GaInAsP LED ($λ\approx$ 1330nm) with an unconventional dopant profile. Compared against that of our control design, which is a conventional $n^+$-$n$-$p^+$ double hetero-junction LED, the dopant profile near the $n$-$p^+$ hetero-structure of the new design displaces the built-in electric field in such a way as to suppress the J02 space charge recombination current. The design principle generalizes to other material systems and could be applicable to efforts to observe and exploit electro-luminescent refrigeration at practical power densities.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Formation and Evolution of Disks around Young Stellar Objects

Recent observations have suggested that circumstellar disks may commonly form around young stellar objects. Although the formation of circumstellar disks can be a natural result of the conservation of angular momentum in the parent cloud, theoretical studies instead show disk formation to be difficult from dense molecular cores magnetized to a realistic level, owing to efficient magnetic braking that transports a large fraction of the angular momentum away from the circumstellar region. We review recent progress in the formation and early evolution of disks around young stellar objects of both low-mass and high-mass, with an emphasis on mechanisms that may bridge the gap between observation and theory, including non-ideal MHD effects and asymmetric perturbations in the collapsing core (e.g., magnetic field misalignment and turbulence). We also address the associated processes of outflow launching and the formation of multiple systems, and discuss possible implications in properties of protoplanetary disks.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Hall Effect in Protostellar Disc Formation and Evolution

The Hall effect is recently shown to be efficient in magnetized dense molecular cores, and could lead to a bimodal formation of rotationally supported discs (RSDs) in the first core phase. However, how such Hall dominated systems evolve in the protostellar accretion phase remains unclear. We carry out 2D axisymmetric simulations including Hall effect and Ohmic dissipation, with realistic magnetic diffusivities computed from our equilibrium chemical network. We find that Hall effect only becomes efficient when the large population of very small grains (VSGs: $\lesssim$10 nm) is removed from the standard MRN size distribution. With such an enhanced Hall effect, however, the bimodality of disc formation does not continue into the main accretion phase. The outer part of the initial $\sim$40 AU disc formed in the anti-aligned configuration (${\bf Ω\cdot B}<0$) flattens into a thin rotationally supported Hall current sheet as Hall effect moves the poloidal magnetic field radially inward relative to matter, leaving only the inner $\lesssim$10--20 AU RSD. In the aligned configuration (${\bf Ω\cdot B}>0$), disc formation is suppressed initially but a counter-rotating disc forms subsequently due to efficient azimuthal Hall drift. The counter-rotating disc first grows to $\sim$30 AU as Hall effect moves the magnetic field radially outward, but only the inner $\lesssim$10 AU RSD is long-lived like in the anti-aligned case. Besides removing VSGs, cosmic ray ionization rate should be below a few 10$^{-16}$ s$^{-1}$ for Hall effect to be efficient in disc formation. We conclude that Hall effect produces small $\lesssim$10--20 AU discs regardless of the polarity of the magnetic field, and that radially outward diffusion of magnetic fields remains crucial for disc formation and growth.

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preprint2020arXiv

iDLG: Improved Deep Leakage from Gradients

It is widely believed that sharing gradients will not leak private training data in distributed learning systems such as Collaborative Learning and Federated Learning, etc. Recently, Zhu et al. presented an approach which shows the possibility to obtain private training data from the publicly shared gradients. In their Deep Leakage from Gradient (DLG) method, they synthesize the dummy data and corresponding labels with the supervision of shared gradients. However, DLG has difficulty in convergence and discovering the ground-truth labels consistently. In this paper, we find that sharing gradients definitely leaks the ground-truth labels. We propose a simple but reliable approach to extract accurate data from the gradients. Particularly, our approach can certainly extract the ground-truth labels as opposed to DLG, hence we name it Improved DLG (iDLG). Our approach is valid for any differentiable model trained with cross-entropy loss over one-hot labels. We mathematically illustrate how our method can extract ground-truth labels from the gradients and empirically demonstrate the advantages over DLG.

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preprint2020arXiv

Impact of magneto-rotational instability on grain growth in protoplanetary disks: I. Relevant turbulence properties

Turbulence in the protoplanetary disks induces collisions between dust grains, and thus facilitates grain growth. We investigate the two fundamental assumptions of the turbulence in obtaining grain collisional velocities -- the kinetic energy spectrum and the turbulence autocorrelation time -- in the context of the turbulence generated by the magneto-rotational instability (MRI). We carry out numerical simulations of the MRI as well as driven turbulence, for a range of physical and numerical parameters. We find that the convergence of the turbulence $α$-parameter does not necessarily imply the convergence of the energy spectrum. The MRI turbulence is largely solenoidal, for which we observe a persistent kinetic energy spectrum of $k^{-4/3}$. The same is obtained for solenoidal driven turbulence with and without magnetic field, over more than 1 dex near the dissipation scale. This power-law slope appears to be converged in terms of numerical resolution, and to be due to the bottleneck effect. The kinetic energy in the MRI turbulence peaks at the fastest growing mode of the MRI. In contrast, the magnetic energy peaks at the dissipation scale. The magnetic energy spectrum in the MRI turbulence does not show a clear power-law range, and is almost constant over approximately 1 dex near the dissipation scale. The turbulence autocorrelation time is nearly constant at large scales, limited by the shearing timescale, and shows a power-law drop close to $k^{-1}$ at small scales, with a slope steeper than that of the eddy crossing time. The deviation from the standard picture of the Kolmogorov turbulence with the injection scale at the disk scale height can potentially have a significant impact on the grain collisional velocities.

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preprint2020arXiv

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&#39;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.

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preprint2020arXiv

PinnerSage: Multi-Modal User Embedding Framework for Recommendations at Pinterest

Latent user representations are widely adopted in the tech industry for powering personalized recommender systems. Most prior work infers a single high dimensional embedding to represent a user, which is a good starting point but falls short in delivering a full understanding of the user&#39;s interests. In this work, we introduce PinnerSage, an end-to-end recommender system that represents each user via multi-modal embeddings and leverages this rich representation of users to provides high quality personalized recommendations. PinnerSage achieves this by clustering users&#39; actions into conceptually coherent clusters with the help of a hierarchical clustering method (Ward) and summarizes the clusters via representative pins (Medoids) for efficiency and interpretability. PinnerSage is deployed in production at Pinterest and we outline the several design decisions that makes it run seamlessly at a very large scale. We conduct several offline and online A/B experiments to show that our method significantly outperforms single embedding methods.

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preprint2020arXiv

Spatial polarization independent parametric upconversion of vectorially structured light

Spatial polarization independent (SPI) parametric conversion is the basis of many optical applications, such as SPI frequency interface for communication channels carried by vector modes and upconversion detection for polarization-resolved imaging. However, realizing such conversion remains a challenge. In this proof-of-principle work, we demonstrated SPI parametric upconversion using a polarization Sagnac nonlinear interferometer based on type-II second-harmonic generation (SHG). Our results show that the vector (including both polarization and intensity) profile and associated SOC state of the vector signal beam could be transferred to the SHG beam with a high fidelity. The principle lays a foundation of SPI frequency interface for quantum/classical channels based on vector modes and also paves the way for upconversion detection of polarization-resolved imaging in Mid-/far-infrared region.

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preprint2020arXiv

Transition from ordered pinched to warped magnetic field on a 100 au scale in the Class 0 protostar B335

We present our observational results of the 0.87 mm polarized dust emission in the Class 0 protostar B335 obtained with the Atacama Large Millimeter/submillimeter Array (ALMA) at a 0.2&#34; (20 au) resolution. We compared our data at 0.87 mm with those at 1.3 mm from the ALMA archive. The observed polarization orientations at the two wavelengths are consistent within the uncertainty, and the polarization percentages are systematically higher at 1.3 mm than 0.87 mm by a factor of ~1.7, suggesting that the polarized emission originates from magnetically aligned dust grains. We inferred the magnetic field orientations from the observed polarization orientations. We found that the magnetic field changes from ordered and highly pinched to more complicated and asymmetric structures within the inner 100 au scale of B335, and the magnetic field connects to the center along the equatorial plane as well as along the directions which are ~40-60 degrees from the equatorial plane. We performed non-ideal MHD simulations of collapsing dense cores. We found that similar magnetic field structures appear in our simulations of dense cores with the magnetic field and rotational axis slightly misaligned by 15 degrees but not in those with the aligned magnetic field and rotational axis. Our results suggest that the midplane of the inner envelope within the inner 100 au scale of B335 could be warped because of the misaligned magnetic field and rotational axis, and the magnetic field could be dragged by the warped accretion flows.

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preprint2019arXiv

Axion-Field-Enabled Nonreciprocal Thermal Radiation in Weyl Semimetals

Objects around us constantly emit and absorb thermal radiation. The emission and absorption processes are governed by two fundamental radiative properties: emissivity and absorptivity.For reciprocal systems, the emissivity and absorptivity are restricted to be equal by Kirchhoff&#39;s law of thermal radiation. This restriction limits the degree of freedom to control thermal radiation and contributes to an intrinsic loss mechanism in photonic energy harvesting systems such assolar cells. Existing approaches to violate Kirchhoff&#39;s law typically utilize conventional magneto-optical effects in the presence of an external magnetic field. However, these approaches require either a strong magnetic field (~3T), or narrow-band resonances under a moderate magnetic field (~0.3T), because the non-reciprocity in conventional magneto-optical effects isusually weak in the thermal wavelength range. Here, we show that the axion electrodynamics in magnetic Weyl semimetals can be used to construct strongly nonreciprocal thermal emitters that near completely violate Kirchhoff&#39;s law over broad angular and frequency ranges, without requiring any external magnetic field. The non-reciprocity moreover is strongly temperature tunable, opening new possibilities for active non-reciprocal devices in controlling thermal radiation.

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preprint2019arXiv

Broadening near-field emission for performance enhancement in thermophotovoltaics

The conventional notion for achieving high efficiency in thermophotovoltaics (TPVs) is to use a monochromatic emission at a photon energy corresponding to the band gap of the cell. Here, we prove theoretically that such a notion is only accurate under idealized conditions, and further show that when non-radiative recombination is taken into account, efficiency improvement can be achieved by broadening the emission spectrum, due to an enhancement in the open-circuit voltage. Broadening the emission spectrum also improves the electrical power density, by increasing the short-circuit current. To practically illustrate these findings, we focus on surface polariton-mediated near-field TPVs. We propose a versatile design strategy for broadening the emission spectrum via stacking of multiple plasmonic thin film layers. As an example, we consider a realistic ITO/InAs TPV, and predict a conversion efficiency of $50\%$ simultaneously with a power density of nearly $80$ W$/\mathrm{cm}^2$ at a $1300$ K emitter temperature. The performance of our proposed system far exceeds previous works in similar systems using a single plasmonic layer emitter.

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preprint2019arXiv

Integrated near-field thermo-photovoltaics for on-demand heat recycling

The energy transferred via thermal radiation between two surfaces separated by nanometers distances (near-field) can be much larger than the blackbody limit. However, realizing a reconfigurable platform that utilizes this energy exchange mechanism to generate electricity in industrial and space applications on-demand, remains a challenge. The challenge lies in designing a platform that can separate two surfaces by a small and tunable gap while simultaneously maintaining a large temperature differential. Here, we present a fully integrated, reconfigurable and scalable platform operating in near-field regime that performs controlled heat extraction and energy recycling. Our platform relies on an integrated nano-electromechanical system (NEMS) that enables precise positioning of a large area thermal emitter within nanometers distances from a room-temperature germanium photodetector to form a thermo-photovoltaic (TPV) cell. We show over an order of magnitude higher power generation $\mathrm{P_{gen} \sim 1.25 \, μW \cdot cm^{-2}}$ from our TPV cell by tuning the gap between a hot emitter ($\mathrm{T_E \sim 880 \, K}$) and the cold photodetector ($\mathrm{T_D \sim 300 \, K}$) from $\mathrm{\sim 500 \, nm}$ to $\mathrm{\sim 100 \, nm}$. The significant enhancement in $\mathrm{P_{gen}}$ at such small distances is a clear indication of near-field heat transfer effect. Our electrostatically controlled NEMS switch consumes negligible tuning power ($\mathrm{P_{gen}/P_{NEMS} \sim 10^4}$) and relies on conventional silicon-based process technologies.

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preprint2019arXiv

Nonreciprocal radiative heat transfer between two planar bodies

We develop an analytical framework for nonreciprocal radiative heat transfer in two-body planar systems. Based on our formalism, we identify effects that are uniquely nonreciprocal in near-field heat transfer in planar systems. We further introduce a general thermodynamic constraint that is applicable for both reciprocal and nonreciprocal planar systems, in agreement with the second law of thermodynamics. We numerically demonstrate our findings in an example system consisting of magneto-optical materials. Our formalism applies to both near- and far-field regimes, opening opportunities for exploiting nonreciprocity in two-body radiative heat transfer systems.

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preprint2018arXiv

A Large-scale Attribute Dataset for Zero-shot Learning

Zero-Shot Learning (ZSL) has attracted huge research attention over the past few years; it aims to learn the new concepts that have never been seen before. In classical ZSL algorithms, attributes are introduced as the intermediate semantic representation to realize the knowledge transfer from seen classes to unseen classes. Previous ZSL algorithms are tested on several benchmark datasets annotated with attributes. However, these datasets are defective in terms of the image distribution and attribute diversity. In addition, we argue that the &#34;co-occurrence bias problem&#34; of existing datasets, which is caused by the biased co-occurrence of objects, significantly hinders models from correctly learning the concept. To overcome these problems, we propose a Large-scale Attribute Dataset (LAD). Our dataset has 78,017 images of 5 super-classes, 230 classes. The image number of LAD is larger than the sum of the four most popular attribute datasets. 359 attributes of visual, semantic and subjective properties are defined and annotated in instance-level. We analyze our dataset by conducting both supervised learning and zero-shot learning tasks. Seven state-of-the-art ZSL algorithms are tested on this new dataset. The experimental results reveal the challenge of implementing zero-shot learning on our dataset.

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preprint2018arXiv

MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning

It is one typical and general topic of learning a good embedding model to efficiently learn the representation coefficients between two spaces/subspaces. To solve this task, $L_{1}$ regularization is widely used for the pursuit of feature selection and avoiding overfitting, and yet the sparse estimation of features in $L_{1}$ regularization may cause the underfitting of training data. $L_{2}$ regularization is also frequently used, but it is a biased estimator. In this paper, we propose the idea that the features consist of three orthogonal parts, \emph{namely} sparse strong signals, dense weak signals and random noise, in which both strong and weak signals contribute to the fitting of data. To facilitate such novel decomposition, \emph{MSplit} LBI is for the first time proposed to realize feature selection and dense estimation simultaneously. We provide theoretical and simulational verification that our method exceeds $L_{1}$ and $L_{2}$ regularization, and extensive experimental results show that our method achieves state-of-the-art performance in the few-shot and zero-shot learning.

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preprint2017arXiv

AI Challenger : A Large-scale Dataset for Going Deeper in Image Understanding

Significant progress has been achieved in Computer Vision by leveraging large-scale image datasets. However, large-scale datasets for complex Computer Vision tasks beyond classification are still limited. This paper proposed a large-scale dataset named AIC (AI Challenger) with three sub-datasets, human keypoint detection (HKD), large-scale attribute dataset (LAD) and image Chinese captioning (ICC). In this dataset, we annotate class labels (LAD), keypoint coordinate (HKD), bounding box (HKD and LAD), attribute (LAD) and caption (ICC). These rich annotations bridge the semantic gap between low-level images and high-level concepts. The proposed dataset is an effective benchmark to evaluate and improve different computational methods. In addition, for related tasks, others can also use our dataset as a new resource to pre-train their models.

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preprint2017arXiv

Zero-Shot Learning posed as a Missing Data Problem

This paper presents a method of zero-shot learning (ZSL) which poses ZSL as the missing data problem, rather than the missing label problem. Specifically, most existing ZSL methods focus on learning mapping functions from the image feature space to the label embedding space. Whereas, the proposed method explores a simple yet effective transductive framework in the reverse way \--- our method estimates data distribution of unseen classes in the image feature space by transferring knowledge from the label embedding space. In experiments, our method outperforms the state-of-the-art on two popular datasets.

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