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Lei Huang

Lei Huang appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

ResearcherUnclaimed source record
Computer VisionMachine Learningastro-ph.HEComputation and Languageastro-ph.GAArtificial Intelligencemath.OCphysics.opticscond-mat.mes-hallInformation Theorymath.ITmath.NAMethodologyphysics.plasm-phastro-ph.IMBiomoleculesComputational Geometrycond-mat.mtrl-scicond-mat.supr-conDistributed, Parallel, and Cluster Computingeess.SPmath.ACmath.MGmath.RAmath.STNumerical AnalysisStatistics TheorySymbolic Computation

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Published work

50 published item(s)

preprint2026arXiv

1.1 kW, 100 Hz room-temperature diode-pumped nanosecond laser by water immersion cooling

We report a room-temperature diode-pumped solid-state laser by water immersion cooling, which delivers a pulse energy of 11 J at the repetition rate of 100 Hz and the pulse duration of 7 ns, while the beam quality factor is 2.6 times the diffraction limit. To the best of our knowledge, this represents the highest performance achieved for room-temperature nanosecond lasers operating above 100 Hz, which demonstrates the great potentials of room-temperature immersion-cooled nanosecond active mirror lasers.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

A finite-termination algorithm for testing copositivity over the positive semidefinite cone

This paper proposes an efficient algorithm for testing copositivity of homogeneous polynomials over the positive semidefinite cone. The algorithm is based on a novel matrix optimization reformulation and requires solving a hierarchy of semidefinite programs. Notably, it always terminates in finitely many iterations. If a homogeneous polynomial is copositive over the positive semidefinite cone, the algorithm provides a certificate; otherwise, it returns a vector that refutes copositivity. Building on a similar idea, we further propose an algorithm to test copositivity over the direct product of the positive semidefinite cone and the nonnegative orthant. Preliminary numerical experiments demonstrate the effectiveness of the proposed methods.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Agentic Recommender System with Hierarchical Belief-State Memory

Memory-augmented LLM agents have advanced personalized recommendation, yet existing approaches universally adopt flat memory representations that conflate ephemeral signals with stable preferences, and none provides a complete lifecycle governing how memory should evolve. We propose MARS (Memory-Augmented Agentic Recommender System), a framework that treats recommendation as a partially observable problem and maintains a structured belief state that progressively abstracts noisy behavioral observations into a compact estimate of user preferences. MARS organizes this belief state into three tiers: event memory buffers raw signals, preference memory maintains fine-grained mutable chunks with explicit strength and evidence tracking, and profile memory distills all preferences into a coherent natural language narrative. A complete lifecycle of six operations -- extraction, reinforcement, weakening, consolidation, forgetting, and resynthesis -- is adaptively scheduled by an LLM-based planner rather than fixed-interval heuristics. Experiments on four InstructRec benchmark domains show that MARS achieves state-of-the-art performance with average improvements of 26.4% in HR@1 and 10.3% in NDCG@10 over the strongest baselines with further gains from agentic scheduling in evolving settings.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Anti-Self-Distillation for Reasoning RL via Pointwise Mutual Information

On-policy self-distillation, where a student is pulled toward a copy of itself conditioned on privileged context (e.g., a verified solution or feedback), offers a promising direction for advancing reasoning capability without a stronger external teacher. Yet in math reasoning the gains are inconsistent, even when the same approach succeeds elsewhere. A pointwise mutual information analysis traces the failure to the privileged context itself: it inflates the teacher's confidence on tokens already implied by the solution (structural connectives, verifiable claims) and deflates it on deliberation tokens ("Wait", "Let", "Maybe") that drive multi-step search. We propose Anti-Self-Distillation (AntiSD), which ascends a divergence between student and teacher rather than descending it: this reverses the per-token sign and yields a naturally bounded advantage in one step. An entropy-triggered gate disables the term once the teacher entropy collapses, completing a drop-in replacement for default self-distillation. Across five models from 4B to 30B parameters on math reasoning benchmarks, AntiSD reaches the GRPO baseline's accuracy in 2 to 10x fewer training steps and improves final accuracy by up to 11.5 points. AntiSD opens a path to scalable self-improvement, where a language model bootstraps its own reasoning through its training signal.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

CAST: Mitigating Object Hallucination in Large Vision-Language Models via Caption-Guided Visual Attention Steering

Although Large Vision-Language Models (LVLMs) have demonstrated remarkable performance on downstream tasks, they frequently produce contents that deviate from visual information, leading to object hallucination. To tackle this, recent works mostly depend on expensive manual annotations and training cost, or decoding strategies which significantly increase inference time. In this work, we observe that LVLMs' attention to visual information is significantly enhanced when answering caption queries compared to non-caption queries. Inspired by this phenomenon, we propose Caption-guided Visual Attention Steering (CAST), a training-free, plug-and-play hallucination mitigation method that leverages the attention activation pattern corresponding to caption queries to enhance LVLMs' visual perception capability. Specifically, we use probing techniques to identify attention heads that are highly sensitive to caption queries and estimate optimized steering directions for their outputs. This steering strengthens LVLM's fine-grained visual perception capabilities, thereby effectively mitigating object hallucination. CAST reduced object hallucination by an average of 6.03% across five widely used LVLMs and five benchmarks including both discriminative and generative tasks, demonstrating state-of-the-art performance while adding little inference cost and preserving other foundational capabilities.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Counting and Entropy Bounds for Structure-Avoiding Spatially-Coupled LDPC Constructions

Designing large coupling memory quasi-cyclic spatially-coupled LDPC (QC-SC-LDPC) codes with low error floors requires eliminating specific harmful substructures (e.g., short cycles) induced by edge spreading and lifting. Building on our work~\cite{r15} that introduced a Clique Lovász Local Lemma (CLLL)-based design principle and a Moser--Tardos (MT)-type constructive approach, this work quantifies the size and structure of the feasible design space. Using the quantitative CLLL, we derive explicit lower bounds on the number of feasible edge-spreading and lifting assignments satisfying a given family of structure-avoidance constraints, and further obtain bounds on the number of non-equivalent solutions under row/column permutations. Moreover, via Rényi entropy bounds for the MT distribution, we provide a computable lower bound on the number of distinct solutions that the MT algorithm can output, giving a concrete diversity guarantee for randomized constructions. Specializations for eliminating 4-cycles yield closed-form bounds as functions of system parameters, offering a principled way to select the memory and lifting degree and to estimate the remaining search space.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Enjoy Your Layer Normalization with the Computational Efficiency of RMSNorm

Layer normalization (LN) is a fundamental component in modern deep learning, but its per-sample centering and scaling introduce non-negligible inference overhead. RMSNorm improves efficiency by removing the centering operation, yet this may discard benefits associated with centering. This paper propose a framework to determine whether an LN in an arbitrary DNN can be replaced by RMSNorm without changing the model function. The key idea is to fold LN's centering operation into upstream general linear layers by enforcing zero-mean outputs through the column-centered constraint (CCC) and column-based weight centering (CBWC). We extend the analysis to arbitrary DNNs, define such LNs as foldable LNs, and develop a graph-based detection algorithm. Our analysis shows that many LNs in widely used architectures are foldable, enabling exact inference-time conversion and end-to-end acceleration of 2% to 12% without changing model predictions. Experiments across multiple task families further show that, when exact equivalence is partially broken in practical training settings, our method remains competitive with vanilla LN while improving efficiency.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Fine-Mem: Fine-Grained Feedback Alignment for Long-Horizon Memory Management

Effective memory management is essential for large language model agents to navigate long-horizon tasks. Recent research has explored using Reinforcement Learning to develop specialized memory manager agents. However, existing approaches rely on final task performance as the primary reward, which results in severe reward sparsity and ineffective credit assignment, providing insufficient guidance for individual memory operations. To this end, we propose Fine-Mem, a unified framework designed for fine-grained feedback alignment. First, we introduce a Chunk-level Step Reward to provide immediate step-level supervision via auxiliary chunk-specific question answering tasks. Second, we devise Evidence-Anchored Reward Attribution to redistribute global rewards by anchoring credit to key memory operations, based on the specific memory items utilized as evidence in reasoning. Together, these components enable stable policy optimization and align local memory operations with the long-term utility of memory. Experiments on Memalpha and MemoryAgentBench demonstrate that Fine-Mem consistently outperforms strong baselines, achieving superior success rates across various sub-tasks. Further analysis reveals its adaptability and strong generalization capabilities across diverse model configurations and backbones.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

From Generic Correlation to Input-Specific Credit in On-Policy Self Distillation

On-policy self-distillation has emerged as a promising paradigm for post-training language models, in which the model conditions on environment feedback to serve as its own teacher, providing dense token-level rewards without external teacher models or step-level annotations. Despite its empirical success, what this reward actually measures and what kind of credit it assigns remain unclear. Under a posterior-compatibility interpretation of feedback conditioning, standard in the implicit-reward literature, we show that the self-distillation token reward is a Bayesian filtering increment whose trajectory sum is exactly the pointwise mutual information between the response and the feedback given the input. This pMI can be raised by input-specific reasoning or by input-generic shortcuts, so we further decompose the teacher log-probability along the input axis. Based on this analysis, we propose CREDIT (Contrastive REward from DIsTillation), which isolates the input-specific component with a batch-contrastive baseline. At the sequence level, CREDIT is a teacher-side surrogate for a contrastive pMI objective that also penalizes responses remaining likely under unrelated inputs. Across coding, scientific reasoning, and tool-use benchmarks on two model families, CREDIT delivers the strongest aggregate performance at negligible additional compute.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Joint Beamforming and Position Optimization for Fluid RIS-aided ISAC Systems

A fluid reconfigurable intelligent surface (fRIS)-aided integrated sensing and communication (ISAC) system is proposed to enhance multi-target sensing and multi-user communication. Unlike the conventional RIS, the fRIS employs movable elements with adjustable positions, offering additional spatial degrees of freedom. In this system, a joint optimization problem is formulated to minimize sensing beampattern mismatch and symbol estimation error. An algorithm based on alternating minimization is devised to handle the resultant non-convex problem, where the subproblems are solved via augmented Lagrangian method, quadratic programming, semidefinite relaxation, and majorization-minimization. A key challenge is that the element positions affect both incident and reflective channels, leading to the high-order composite objective functions. As a remedy, the high-order terms are transformed into linear and linear-difference forms by exploiting the structural characteristics of fRIS and the channels. Numerical results demonstrate the superiority of the proposed scheme over conventional RIS-aided ISAC and other benchmarks.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Lagrange multiplier expressions for matrix polynomial optimization and tight relaxations

This paper studies matrix constrained polynomial optimization. We investigate how to get explicit expressions for Lagrange multiplier matrices from the first order optimality conditions. The existence of these expressions can be shown under the nondegeneracy condition. Using Lagrange multiplier matrix expressions, we propose a strengthened Moment-SOS hierarchy for solving matrix polynomial optimization. Under some general assumptions, we show that this strengthened hierarchy is tight, or equivalently, it has finite convergence. We also study how to detect tightness and how to extract optimizers. Numerical experiments are provided to show the efficiency of the strengthened hierarchy.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Microwave vortex beam lasing via photonic time crystals

Microwave lasing carrying orbital angular momentum (OAM) holds significant potential for advanced applications in fields such as high-capacity communications, precision sensing, and radar imaging. However, conventional approaches to masers fail to produce emission with embedded OAM. The recent emergence of photonic time crystals (PTCs)-artificially structured media with periodically varying electromagnetic properties in time-offers a paradigm shift toward resonance-free lasing without the need for gain media. Yet, pioneering PTC designs have been based on three-dimensional bulk structures, which lack a surface-emitting configuration, and do not possess the capability to modulate OAM, thus hindering the realization of surface-emitted PTC masing that carries OAM. Here, we report the first experimental demonstration of non-resonant, gain medium-free, and surface-emitted microwave vortex beam lasing OAM using ring-shaped PTCs. By developing a multiplier-driven time-varying metamaterial that achieves over 100% equivalent permittivity modulation depth, we establish momentum bandgaps (k gaps) with sufficient bandwidth to overcome intrinsic losses and enable self-sustained coherent microwave amplification. Furthermore, space-time modulation induces non-reciprocity between clockwise and counterclockwise k gap modes within the circularly symmetric PTC structure, facilitating the selective generation of microwave lasing carrying OAM-a capability beyond the reach of conventional maser technologies. Our work bridges PTC physics with coherent OAM-carrying microwave emission, establishing a transformative platform for next-generation wireless communications, advanced sensing systems, and OAM-based technologies.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Weighted least squares estimation by multivariate-dependent weights for linear regression models

Multivariate linear regression models often face the problem of heteroscedasticity caused by multiple explanatory variables. The weighted least squares estimation with univariate-dependent weights has limitations in constructing weight functions. Therefore, this paper proposes a multivariate dependent weighted least squares estimation method. By constructing a linear combination of explanatory variables and maximizing their Spearman rank correlation coefficient with the absolute residual value, combined with maximum likelihood method to depict heteroscedasticity, it can comprehensively reflect the trend of variance changes in the random error and improve the accuracy of the model. This paper demonstrates that the optimal linear combination exponent estimator for heteroscedastic volatility obtained by our algorithm possesses consistency and asymptotic normality. In the simulation experiment, three scenarios of heteroscedasticity were designed, and the comparison showed that the proposed method was superior to the univariate-dependent weighting method in parameter estimation and model prediction. In the real data applications, the proposed method was applied to two real-world datasets about consumer spending in China and housing prices in Boston. From the perspectives of MAE, RSE, cross-validation, and fitting performance, its accuracy and stability were verified in terms of model prediction, interval estimation, and generalization ability. Additionally, the proposed method demonstrated relative advantages in fitting data with large fluctuations. This study provides an effective new approach for dealing with heteroscedasticity in multivariate linear regression.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Bi-level Doubly Variational Learning for Energy-based Latent Variable Models

Energy-based latent variable models (EBLVMs) are more expressive than conventional energy-based models. However, its potential on visual tasks are limited by its training process based on maximum likelihood estimate that requires sampling from two intractable distributions. In this paper, we propose Bi-level doubly variational learning (BiDVL), which is based on a new bi-level optimization framework and two tractable variational distributions to facilitate learning EBLVMs. Particularly, we lead a decoupled EBLVM consisting of a marginal energy-based distribution and a structural posterior to handle the difficulties when learning deep EBLVMs on images. By choosing a symmetric KL divergence in the lower level of our framework, a compact BiDVL for visual tasks can be obtained. Our model achieves impressive image generation performance over related works. It also demonstrates the significant capacity of testing image reconstruction and out-of-distribution detection.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Delving into the Estimation Shift of Batch Normalization in a Network

Batch normalization (BN) is a milestone technique in deep learning. It normalizes the activation using mini-batch statistics during training but the estimated population statistics during inference. This paper focuses on investigating the estimation of population statistics. We define the estimation shift magnitude of BN to quantitatively measure the difference between its estimated population statistics and expected ones. Our primary observation is that the estimation shift can be accumulated due to the stack of BN in a network, which has detriment effects for the test performance. We further find a batch-free normalization (BFN) can block such an accumulation of estimation shift. These observations motivate our design of XBNBlock that replace one BN with BFN in the bottleneck block of residual-style networks. Experiments on the ImageNet and COCO benchmarks show that XBNBlock consistently improves the performance of different architectures, including ResNet and ResNeXt, by a significant margin and seems to be more robust to distribution shift.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Generalized truncated moment problems with unbounded sets

This paper studies generalized truncated moment problems with unbounded sets. First, we study geometric properties of the truncated moment cone and its dual cone of nonnegative polynomials. By the technique of homogenization, we give a convergent hierarchy of Moment-SOS relaxations for approximating these cones. With them, we give a Moment-SOS method for solving generalized truncated moment problems with unbounded sets. Finitely atomic representing measures, or certificates for their nonexistence, can be obtained by the proposed method. Numerical experiments and applications are also given.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

MDM: Molecular Diffusion Model for 3D Molecule Generation

Molecule generation, especially generating 3D molecular geometries from scratch (i.e., 3D \textit{de novo} generation), has become a fundamental task in drug designs. Existing diffusion-based 3D molecule generation methods could suffer from unsatisfactory performances, especially when generating large molecules. At the same time, the generated molecules lack enough diversity. This paper proposes a novel diffusion model to address those two challenges. First, interatomic relations are not in molecules' 3D point cloud representations. Thus, it is difficult for existing generative models to capture the potential interatomic forces and abundant local constraints. To tackle this challenge, we propose to augment the potential interatomic forces and further involve dual equivariant encoders to encode interatomic forces of different strengths. Second, existing diffusion-based models essentially shift elements in geometry along the gradient of data density. Such a process lacks enough exploration in the intermediate steps of the Langevin dynamics. To address this issue, we introduce a distributional controlling variable in each diffusion/reverse step to enforce thorough explorations and further improve generation diversity. Extensive experiments on multiple benchmarks demonstrate that the proposed model significantly outperforms existing methods for both unconditional and conditional generation tasks. We also conduct case studies to help understand the physicochemical properties of the generated molecules.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Moire quasi-bound states in the continuum

The novel physics of twisted bilayer graphene has motivated extensive studies of magic-angle flat bands hosted by moiré structures in electronic, photonic and acoustic systems. On the other hand, bound states in the continuum (BICs) have also attracted great attention in recent years because of their potential applications in the field of designing superior optical devices. Here, we combine these two independent concepts to construct a new optical state in a twisted bilayer photonic crystal slab, which is called as moiré quasi-BIC, and numerically demonstrate that such an exotic optical state possesses dual characteristics of moiré flat bands and quasi-BICs. To illustrate the mechanism for the formation of moiré flat bands, we develop an effective model at the center of the Brillouin zone and show that moiré flat bands could be fulfilled by balancing the interlayer coupling strength and the twist angle around the band edge above the light line. Moreover, by decreasing the twist angle of moiré photonic crystal slabs with flat bands, it is shown that the moiré flat-band mode at the Brillouin center gradually approaches a perfect BIC, where the total radiation loss from all diffraction channels is significantly suppressed. To clarify the advantage of moiré quasi-BICs, enhanced second-harmonic generation (SHG) is numerically proven with a wide-angle optical source. The efficiency of SHG assisted by designed moiré quasi-BICs can be greatly improved compared with that based on dispersive quasi-BICs with similar quality factors.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Revisiting Domain Generalized Stereo Matching Networks from a Feature Consistency Perspective

Despite recent stereo matching networks achieving impressive performance given sufficient training data, they suffer from domain shifts and generalize poorly to unseen domains. We argue that maintaining feature consistency between matching pixels is a vital factor for promoting the generalization capability of stereo matching networks, which has not been adequately considered. Here we address this issue by proposing a simple pixel-wise contrastive learning across the viewpoints. The stereo contrastive feature loss function explicitly constrains the consistency between learned features of matching pixel pairs which are observations of the same 3D points. A stereo selective whitening loss is further introduced to better preserve the stereo feature consistency across domains, which decorrelates stereo features from stereo viewpoint-specific style information. Counter-intuitively, the generalization of feature consistency between two viewpoints in the same scene translates to the generalization of stereo matching performance to unseen domains. Our method is generic in nature as it can be easily embedded into existing stereo networks and does not require access to the samples in the target domain. When trained on synthetic data and generalized to four real-world testing sets, our method achieves superior performance over several state-of-the-art networks.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Towards Better Understanding with Uniformity and Explicit Regularization of Embeddings in Embedding-based Neural Topic Models

Embedding-based neural topic models could explicitly represent words and topics by embedding them to a homogeneous feature space, which shows higher interpretability. However, there are no explicit constraints for the training of embeddings, leading to a larger optimization space. Also, a clear description of the changes in embeddings and the impact on model performance is still lacking. In this paper, we propose an embedding regularized neural topic model, which applies the specially designed training constraints on word embedding and topic embedding to reduce the optimization space of parameters. To reveal the changes and roles of embeddings, we introduce \textbf{uniformity} into the embedding-based neural topic model as the evaluation metric of embedding space. On this basis, we describe how embeddings tend to change during training via the changes in the uniformity of embeddings. Furthermore, we demonstrate the impact of changes in embeddings in embedding-based neural topic models through ablation studies. The results of experiments on two mainstream datasets indicate that our model significantly outperforms baseline models in terms of the harmony between topic quality and document modeling. This work is the first attempt to exploit uniformity to explore changes in embeddings of embedding-based neural topic models and their impact on model performance to the best of our knowledge.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

EGFI: Drug-Drug Interaction Extraction and Generation with Fusion of Enriched Entity and Sentence Information

The rapid growth in literature accumulates diverse and yet comprehensive biomedical knowledge hidden to be mined such as drug interactions. However, it is difficult to extract the heterogeneous knowledge to retrieve or even discover the latest and novel knowledge in an efficient manner. To address such a problem, we propose EGFI for extracting and consolidating drug interactions from large-scale medical literature text data. Specifically, EGFI consists of two parts: classification and generation. In the classification part, EGFI encompasses the language model BioBERT which has been comprehensively pre-trained on biomedical corpus. In particular, we propose the multi-head attention mechanism and pack BiGRU to fuse multiple semantic information for rigorous context modeling. In the generation part, EGFI utilizes another pre-trained language model BioGPT-2 where the generation sentences are selected based on filtering rules. We evaluated the classification part on "DDIs 2013" dataset and "DTIs" dataset, achieving the FI score of 0.842 and 0.720 respectively. Moreover, we applied the classification part to distinguish high-quality generated sentences and verified with the exiting growth truth to confirm the filtered sentences. The generated sentences that are not recorded in DrugBank and DDIs 2013 dataset also demonstrate the potential of EGFI to identify novel drug relationships.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Many-to-One Distribution Learning and K-Nearest Neighbor Smoothing for Thoracic Disease Identification

Chest X-rays are an important and accessible clinical imaging tool for the detection of many thoracic diseases. Over the past decade, deep learning, with a focus on the convolutional neural network (CNN), has become the most powerful computer-aided diagnosis technology for improving disease identification performance. However, training an effective and robust deep CNN usually requires a large amount of data with high annotation quality. For chest X-ray imaging, annotating large-scale data requires professional domain knowledge and is time-consuming. Thus, existing public chest X-ray datasets usually adopt language pattern based methods to automatically mine labels from reports. However, this results in label uncertainty and inconsistency. In this paper, we propose many-to-one distribution learning (MODL) and K-nearest neighbor smoothing (KNNS) methods from two perspectives to improve a single model's disease identification performance, rather than focusing on an ensemble of models. MODL integrates multiple models to obtain a soft label distribution for optimizing the single target model, which can reduce the effects of original label uncertainty. Moreover, KNNS aims to enhance the robustness of the target model to provide consistent predictions on images with similar medical findings. Extensive experiments on the public NIH Chest X-ray and CheXpert datasets show that our model achieves consistent improvements over the state-of-the-art methods.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

An Efficient Agreement Mechanism in CapsNets By Pairwise Product

Capsule networks (CapsNets) are capable of modeling visual hierarchical relationships, which is achieved by the "routing-by-agreement" mechanism. This paper proposes a pairwise agreement mechanism to build capsules, inspired by the feature interactions of factorization machines (FMs). The proposed method has a much lower computation complexity. We further proposed a new CapsNet architecture that combines the strengths of residual networks in representing low-level visual features and CapsNets in modeling the relationships of parts to wholes. We conduct comprehensive experiments to compare the routing algorithms, including dynamic routing, EM routing, and our proposed FM agreement, based on both architectures of original CapsNet and our proposed one, and the results show that our method achieves both excellent performance and efficiency under a variety of situations.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

An Investigation into the Stochasticity of Batch Whitening

Batch Normalization (BN) is extensively employed in various network architectures by performing standardization within mini-batches. A full understanding of the process has been a central target in the deep learning communities. Unlike existing works, which usually only analyze the standardization operation, this paper investigates the more general Batch Whitening (BW). Our work originates from the observation that while various whitening transformations equivalently improve the conditioning, they show significantly different behaviors in discriminative scenarios and training Generative Adversarial Networks (GANs). We attribute this phenomenon to the stochasticity that BW introduces. We quantitatively investigate the stochasticity of different whitening transformations and show that it correlates well with the optimization behaviors during training. We also investigate how stochasticity relates to the estimation of population statistics during inference. Based on our analysis, we provide a framework for designing and comparing BW algorithms in different scenarios. Our proposed BW algorithm improves the residual networks by a significant margin on ImageNet classification. Besides, we show that the stochasticity of BW can improve the GAN's performance with, however, the sacrifice of the training stability.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Controllable Orthogonalization in Training DNNs

Orthogonality is widely used for training deep neural networks (DNNs) due to its ability to maintain all singular values of the Jacobian close to 1 and reduce redundancy in representation. This paper proposes a computationally efficient and numerically stable orthogonalization method using Newton's iteration (ONI), to learn a layer-wise orthogonal weight matrix in DNNs. ONI works by iteratively stretching the singular values of a weight matrix towards 1. This property enables it to control the orthogonality of a weight matrix by its number of iterations. We show that our method improves the performance of image classification networks by effectively controlling the orthogonality to provide an optimal tradeoff between optimization benefits and representational capacity reduction. We also show that ONI stabilizes the training of generative adversarial networks (GANs) by maintaining the Lipschitz continuity of a network, similar to spectral normalization (SN), and further outperforms SN by providing controllable orthogonality.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Convolutional Neural Network Training with Distributed K-FAC

Training neural networks with many processors can reduce time-to-solution; however, it is challenging to maintain convergence and efficiency at large scales. The Kronecker-factored Approximate Curvature (K-FAC) was recently proposed as an approximation of the Fisher Information Matrix that can be used in natural gradient optimizers. We investigate here a scalable K-FAC design and its applicability in convolutional neural network (CNN) training at scale. We study optimization techniques such as layer-wise distribution strategies, inverse-free second-order gradient evaluation, and dynamic K-FAC update decoupling to reduce training time while preserving convergence. We use residual neural networks (ResNet) applied to the CIFAR-10 and ImageNet-1k datasets to evaluate the correctness and scalability of our K-FAC gradient preconditioner. With ResNet-50 on the ImageNet-1k dataset, our distributed K-FAC implementation converges to the 75.9% MLPerf baseline in 18-25% less time than does the classic stochastic gradient descent (SGD) optimizer across scales on a GPU cluster.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Invertible Zero-Shot Recognition Flows

Deep generative models have been successfully applied to Zero-Shot Learning (ZSL) recently. However, the underlying drawbacks of GANs and VAEs (e.g., the hardness of training with ZSL-oriented regularizers and the limited generation quality) hinder the existing generative ZSL models from fully bypassing the seen-unseen bias. To tackle the above limitations, for the first time, this work incorporates a new family of generative models (i.e., flow-based models) into ZSL. The proposed Invertible Zero-shot Flow (IZF) learns factorized data embeddings (i.e., the semantic factors and the non-semantic ones) with the forward pass of an invertible flow network, while the reverse pass generates data samples. This procedure theoretically extends conventional generative flows to a factorized conditional scheme. To explicitly solve the bias problem, our model enlarges the seen-unseen distributional discrepancy based on negative sample-based distance measurement. Notably, IZF works flexibly with either a naive Bayesian classifier or a held-out trainable one for zero-shot recognition. Experiments on widely-adopted ZSL benchmarks demonstrate the significant performance gain of IZF over existing methods, in both classic and generalized settings.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Layer-wise Conditioning Analysis in Exploring the Learning Dynamics of DNNs

Conditioning analysis uncovers the landscape of an optimization objective by exploring the spectrum of its curvature matrix. This has been well explored theoretically for linear models. We extend this analysis to deep neural networks (DNNs) in order to investigate their learning dynamics. To this end, we propose layer-wise conditioning analysis, which explores the optimization landscape with respect to each layer independently. Such an analysis is theoretically supported under mild assumptions that approximately hold in practice. Based on our analysis, we show that batch normalization (BN) can stabilize the training, but sometimes result in the false impression of a local minimum, which has detrimental effects on the learning. Besides, we experimentally observe that BN can improve the layer-wise conditioning of the optimization problem. Finally, we find that the last linear layer of a very deep residual network displays ill-conditioned behavior. We solve this problem by only adding one BN layer before the last linear layer, which achieves improved performance over the original and pre-activation residual networks.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

The Medical Scribe: Corpus Development and Model Performance Analyses

There is a growing interest in creating tools to assist in clinical note generation using the audio of provider-patient encounters. Motivated by this goal and with the help of providers and medical scribes, we developed an annotation scheme to extract relevant clinical concepts. We used this annotation scheme to label a corpus of about 6k clinical encounters. This was used to train a state-of-the-art tagging model. We report ontologies, labeling results, model performances, and detailed analyses of the results. Our results show that the entities related to medications can be extracted with a relatively high accuracy of 0.90 F-score, followed by symptoms at 0.72 F-score, and conditions at 0.57 F-score. In our task, we not only identify where the symptoms are mentioned but also map them to canonical forms as they appear in the clinical notes. Of the different types of errors, in about 19-38% of the cases, we find that the model output was correct, and about 17-32% of the errors do not impact the clinical note. Taken together, the models developed in this work are more useful than the F-scores reflect, making it a promising approach for practical applications.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Topic Detection and Summarization of User Reviews

A massive amount of reviews are generated daily from various platforms. It is impossible for people to read through tons of reviews and to obtain useful information. Automatic summarizing customer reviews thus is important for identifying and extracting the essential information to help users to obtain the gist of the data. However, as customer reviews are typically short, informal, and multifaceted, it is extremely challenging to generate topic-wise summarization.While there are several studies aims to solve this issue, they are heuristic methods that are developed only utilizing customer reviews. Unlike existing method, we propose an effective new summarization method by analyzing both reviews and summaries.To do that, we first segment reviews and summaries into individual sentiments. As the sentiments are typically short, we combine sentiments talking about the same aspect into a single document and apply topic modeling method to identify hidden topics among customer reviews and summaries. Sentiment analysis is employed to distinguish positive and negative opinions among each detected topic. A classifier is also introduced to distinguish the writing pattern of summaries and that of customer reviews. Finally, sentiments are selected to generate the summarization based on their topic relevance, sentiment analysis score and the writing pattern. To test our method, a new dataset comprising product reviews and summaries about 1028 products are collected from Amazon and CNET. Experimental results show the effectiveness of our method compared with other methods.

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preprint2020arXiv

Toward a Full MHD Jet Model of Spinning Black Holes--II: Kinematics and Application to the M87 Jet

In this paper, we investigate the magnetohydrodynamical structure of a jet powered by a spinning black hole, where electromagnetic fields and fluid motion are governed by the Grad-Shafranov equation and the Bernoulli equation, respectively. Assuming steady and axisymmetric jet structure, the global solution is uniquely determined with prescribed plasma loading into the jet and the poloidal shape of the outmost magnetic field line. We apply this model to the jet in the center of nearby radio galaxy M87, and we find it can naturally explain the slow flow acceleration and the flow velocity stratification within $10^5$ gravitational radii from the central black hole. In particular, we find the extremal black hole spin is disfavored by the flow velocity measurements, if the plasma loading to the jet is dominated by the electron/positron pair production at the jet base.

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preprint2020arXiv

Unbiased Scene Graph Generation via Rich and Fair Semantic Extraction

Extracting graph representation of visual scenes in image is a challenging task in computer vision. Although there has been encouraging progress of scene graph generation in the past decade, we surprisingly find that the performance of existing approaches is largely limited by the strong biases, which mainly stem from (1) unconsciously assuming relations with certain semantic properties such as symmetric and (2) imbalanced annotations over different relations. To alleviate the negative effects of these biases, we proposed a new and simple architecture named Rich and Fair semantic extraction network (RiFa for short), to not only capture rich semantic properties of the relations, but also fairly predict relations with different scale of annotations. Using pseudo-siamese networks, RiFa embeds the subject and object respectively to distinguish their semantic differences and meanwhile preserve their underlying semantic properties. Then, it further predicts subject-object relations based on both the visual and semantic features of entities under certain contextual area, and fairly ranks the relation predictions for those with a few annotations. Experiments on the popular Visual Genome dataset show that RiFa achieves state-of-the-art performance under several challenging settings of scene graph task. Especially, it performs significantly better on capturing different semantic properties of relations, and obtains the best overall per relation performance.

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preprint2016arXiv

Phase Retrieval Using Feasible Point Pursuit: Algorithms and Cramér-Rao Bound

Reconstructing a signal from squared linear (rank-one quadratic) measurements is a challenging problem with important applications in optics and imaging, where it is known as phase retrieval. This paper proposes two new phase retrieval algorithms based on non-convex quadratically constrained quadratic programming (QCQP) formulations, and a recently proposed approximation technique dubbed feasible point pursuit (FPP). The first is designed for uniformly distributed bounded measurement errors, such as those arising from high-rate quantization (B-FPP). The second is designed for Gaussian measurement errors, using a least squares criterion (LS-FPP). Their performance is measured against state-of-the-art algorithms and the Cramér-Rao bound (CRB), which is also derived here. Simulations show that LS-FPP outperforms the state-of-art and operates close to the CRB. Compact CRB expressions, properties, and insights are obtained by explicitly computing the CRB in various special cases -- including when the signal of interest admits a sparse parametrization, using harmonic retrieval as an example.

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preprint2016arXiv

Twist-induced Magnetosphere Reconfiguration for Intermittent Pulsars

We propose that the magnetosphere reconfiguration induced by magnetic twists in the closed field line region can account for the mode-switching of intermittent pulsars. We carefully investigate the properties of axisymmetric force-free pulsar magnetospheres with magnetic twists in closed field line region around the polar caps. The magnetosphere with twisted closed lines leads to enhanced spin-down rates. The enhancement in spin-down rate depends on the size of region with twisted closed lines. Typically, it is increased by a factor of $\sim2$, which is consistent with the intermittent pulsars' spin down behavior during the `off' and `on' states. We find there is a threshold of maximal twist angle $Δϕ_{\rm thres}\sim1$. The magnetosphere is stable only if the closed line twist angle is less than $Δϕ_{\rm thres}$. Beyond this value, the magnetosphere becomes unstable and gets untwisted. The spin-down rate would reduce to its off-state value. The quasi-periodicity in spin-down rate change can be explained by long-term activities in star's crust and the untwisting induced by MHD instability. The estimated duration time of on-state is about one week, consistent with observations. Due to the MHD instability, there exists an upper limit for the spin down ratio ($f\sim3$) between the on-state and the off-state, if the Y-point remains at the light cylinder.

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preprint2015arXiv

Three-Dimensional Projective Geometry with Geometric Algebra

The line geometric model of 3-D projective geometry has the nice property that the Lie algebra sl(4) of 3-D projective transformations is isomorphic to the bivector algebra of CL(3,3), and line geometry is closely related to the classical screw theory for 3-D rigid-body motions. The canonical homomorphism from SL(4) to Spin(3,3) is not satisfying because it is not surjective, and the projective transformations of negative determinant do induce orthogonal transformations in the Plücker coordinate space of lines. This paper presents our contributions in developing a rigorous and convenient algebraic framework for the study of 3-D projective geometry with Clifford algebra. To overcome the unsatisfying defects of the Plücker correspondence, we propose a group Pin^{sp}(3,3) with Pin(3,3) as its normal subgroup, to quadruple-cover the group of 3-D projective transformations and polarities. We construct spinors in factored form that generate 3-D reflections and rigid-body motions, and extend screw algebra from the Lie algebra of rigid-body motions to other 6-D Lie subalgebras of sl(4), and construct the corresponding cross products and virtual works.

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preprint2014arXiv

Magnetar Giant Flares in Multipolar Magnetic Fields --- I. Fully and Partially Open Eruptions of Flux Ropes

We propose a catastrophic eruption model for magnetar's enormous energy release during giant flares, in which a toroidal and helically twisted flux rope is embedded within a force-free magnetosphere. The flux rope stays in stable equilibrium states initially and evolves quasi-statically. Upon the loss of equilibrium point is reached, the flux rope cannot sustain the stable equilibrium states and erupts catastrophically. During the process, the magnetic energy stored in the magnetosphere is rapidly released as the result of destabilization of global magnetic topology. The magnetospheric energy that could be accumulated is of vital importance for the outbursts of magnetars. We carefully establish the fully open fields and partially open fields for various boundary conditions at the magnetar surface and study the relevant energy thresholds. By investigating the magnetic energy accumulated at the critical catastrophic point, we find that it is possible to drive fully open eruptions for dipole dominated background fields. Nevertheless, it is hard to generate fully open magnetic eruptions for multipolar background fields. Given the observational importance of the multipolar magnetic fields in the vicinity of the magnetar surface, it would be worthwhile to explore the possibility of the alternative eruption approach in multipolar background fields. Fortunately, we find that flux ropes may give rise to partially open eruptions in the multipolar fields, which involve only partial opening up of background fields. The energy release fractions are greater for cases with central-arcaded multipoles than those with central-caved multipoles emerged in background fields. Eruptions would fail only when the centrally-caved multipoles become extremely strong.

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preprint2014arXiv

Magnetar Giant Flares in Multipolar Magnetic Fields --- II. Flux Rope Eruptions With Current Sheets

We propose a physical mechanism to explain giant flares and radio afterglows in terms of a magnetospheric model containing both a helically twisted flux rope and a current sheet (CS). With the appearance of CS, we solve a mixed boundary value problem to get the magnetospheric field based on a domain decomposition method. We investigate properties of the equilibrium curve of the flux rope when the CS is present in background multipolar fields. In response to the variations at the magnetar surface, it quasi-statically evolves in stable equilibrium states. The loss of equilibrium occurs at a critical point and, beyond that point, it erupts catastrophically. New features show up when the CS is considered. Especially, we find two kinds of physical behaviors, i.e., catastrophic state transition and catastrophic escape. Magnetic energy would be released during state transitions. The released magnetic energy is sufficient to drive giant flares. The flux rope would go away from the magnetar quasi-statically, which is inconsistent with the radio afterglow. Fortunately, in the latter case, i.e., the catastrophic escape, the flux rope could escape the magnetar and go to infinity in a dynamical way. This is more consistent with radio afterglow observations of giant flares. We find that the minor radius of flux rope has important implications for its eruption. Flux ropes with larger minor radius are more prone to erupt. We stress that the CS provides an ideal place for magnetic reconnection, which would further enhance the energy release during eruptions.

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preprint2014arXiv

Metallicity and Star Formation Activities of the Interacting System Arp 86 from Observation with MOS on Xinglong 2.16m Telescope

We present an analysis of the metallicity and star formation activities of HII regions in the interacting system Arp 86, based on the first scientific observation of the multi-object spectroscopy on the 2.16m Telescope at Xinglong Observatory. We find that the oxygen abundance gradient in Arp 86 is flatter than that in normal disk galaxies, which confirms that gas inflows caused by tidal forces during encounters can flatten the metallicity distributions in galaxies. The companion galaxy NGC 7752 is currently experiencing a galaxy-wide starburst with higher surface density of star formation rate than the main galaxy NGC 7753, which can be explained that the companion galaxy is more susceptible to the effects of interaction than the primary. We also find that the galaxy 2MASX J23470758+2926531 has similar abundance and star formation properties to NGC 7753, and may be a part of the Arp 86 system.

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preprint2014arXiv

Varying-smoother models for functional responses

This paper studies estimation of a smooth function $f(t,s)$ when we are given functional responses of the form $f(t,\cdot)$ + error, but scientific interest centers on the collection of functions $f(\cdot,s)$ for different $s$. The motivation comes from studies of human brain development, in which $t$ denotes age whereas $s$ refers to brain locations. Analogously to varying-coefficient models, in which the mean response is linear in $t$, the "varying-smoother" models that we consider exhibit nonlinear dependence on $t$ that varies smoothly with $s$. We discuss three approaches to estimating varying-smoother models: (a) methods that employ a tensor product penalty; (b) an approach based on smoothed functional principal component scores; and (c) two-step methods consisting of an initial smooth with respect to $t$ at each $s$, followed by a postprocessing step. For the first approach, we derive an exact expression for a penalty proposed by Wood, and an adaptive penalty that allows smoothness to vary more flexibly with $s$. We also develop "pointwise degrees of freedom," a new tool for studying the complexity of estimates of $f(\cdot,s)$ at each $s$. The three approaches to varying-smoother models are compared in simulations and with a diffusion tensor imaging data set.

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preprint2013arXiv

Magnetar Giant Flares and Their Precursors --- Flux Rope Eruptions with Current Sheets

We propose a catastrophic magnetospheric model for magnetar precursors and their successive giant flares. Axisymmetric models of the magnetosphere, which contain both a helically twisted flux rope and a current sheet, are established based on force-free field configurations. In this model, the helically twisted flux rope would lose its equilibrium and erupt abruptly in response to the slow and quasi-static variations at the ultra-strongly magnetized neutron star's surface. In a previous model without current sheets, only one critical point exists in the flux rope equilibrium curve. New features show up in the equilibrium curves for the flux rope when current sheets appear in the magnetosphere. The causal connection between the precursor and the giant flare, as well as the temporary re-entry of the quiescent state between the precursor and the giant flare, can be naturally explained. Magnetic energy would be released during the catastrophic state transitions. The detailed energetics of the model are also discussed. The current sheet created by the catastrophic loss of equilibrium of the flux rope provides an ideal place for magnetic reconnection. We point out the importance of magnetic reconnection for further enhancement of the energy release during eruptions.

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preprint2012arXiv

Conductance of single-atom magnetic junctions: A first-principles study

We present a first-principles investigation to show that the contact conductance of a half conductance quantum ($G_0/2$) found previously does not generally hold for single-atom magnetic junctions composed of a tip and an adatom dsorbed on a surface. The contact conductance of the Ni-Co/Co(111) junction is approximately $G_0/2$, while for the Co-Co/Co(111), Ni-Ni/Ni(111), and Ni-Ni/Ni(001) junctions the contact conductances are 0.80$G_0$, 1.55$G_0$, and 1.77$G_0$, respectively. The deviation from $G_0/2$ is mainly caused by the variation of the spin-down conductance largely determined by the minority $d$ orbitals, as the spin-up one changes little for different junctions.

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preprint2011arXiv

Constraining the Flaring Region of Sagittarius A* By 1.3mm VLBI Measurements

We use a model of an accretion flow coupled with an emergent flare to interpret the latest 1.3mm VLBI measurements for Sagittarius A*. The visibility data constrained the distances from the flare center to the black hole center as $d_{\rm EW}\lesssim20{\rm R_g}$ and $d_{\rm NS}\lesssim80{\rm R_g}$ in the East-West and North-South directions, respectively. If interpreted by the hot-spot model, the flare was preferred to pass in front of the black hole at a radius much larger than $d_{\rm EW}$. If interpreted by the episodic jet launched from a nearly edge-on hot accretion flow, the flare was preferred to be ejected with $θ_{\rm j}\gtrsim40^\circ$ off the black hole rotating axis. This method can be generalized to help us understand future sub-millimeter VLBI observations, and study the millimeter/sub-millimeter variabilities in the vicinity of the Galactic Center supermassive black hole.

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preprint2011arXiv

Faraday Conversion and Rotation In Uniformly Magnetized Relativistic Plasmas

We provide precise fitting formulae for Faraday conversion and rotation coefficients in uniformly magnetized relativistic plasma. The formulae are immediately applicable to Rotation Measure and Circular Polarization (CP) production in jets and hot accretion flows. We show the recipe and results for arbitrary isotropic particle distributions, in particular thermal and power-law. The exact Faraday conversion coefficient is found to approach zero with the increasing particle energy. The non-linear corrections of Faraday conversion and rotation coefficients are found essential for reliable CP inter- pretation of Sgr A*.

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preprint2010arXiv

A new conception for computing gröbner basis and its applications

This paper presents a conception for computing gröbner basis. We convert some of gröbner-computing algorithms, e.g., F5, extended F5 and GWV algorithms into a special type of algorithm. The new algorithm's finite termination problem can be described by equivalent conditions, so all the above algorithms can be determined when they terminate finitely. At last, a new criterion is presented. It is an improvement for the Rewritten and Signature Criterion.

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preprint2010arXiv

General relativistic polarized radiative transfer: building a dynamics-observations interface

The rising amount of polarized observations of relativistic sources requires the correct theory for proper model fitting. The equations for general relativistic (GR) polarized radiative transfer are derived starting from the Boltzmann equation and basic ideas of general relativity. The derivation is aimed at providing a practical guide to reproducing the synchrotron part of radio & sub-mm emission from low luminosity active galactic nuclei (LLAGNs), in particular Sgr A*, and jets. The recipe for fast exact calculation of cyclo-synchrotron emissivities, absorptivities, Faraday rotation and conversion coefficients is given for isotropic particle distributions. The multitude of physical effects influencing simulated spectrum is discussed. The application of the prescribed technique is necessary to determine the black hole (BH) spin in LLAGNs, constraining it with all observations of total flux, linear and circular polarization fractions, and electric vector position angle as functions of the observed frequency.

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preprint2009arXiv

Images of the radiatively inefficient accretion flow surrounding a Kerr black hole: application in Sgr A*

In fully general relativity, we calculate the images of the radiatively inefficient accretion flow (RIAF) surrounding a Kerr black hole with arbitrary spins, inclination angles, and observational wavelengths. For the same initial conditions, such as the fixed accretion rate, it is found that the intrinsic size and radiation intensity of the images become larger, but the images become more compact in the inner region, while the size of the black hole shadow decreases with the increase of the black hole spin. With the increase of the inclination angles, the shapes of the black hole shadows change and become smaller, even disappear at all due to the obscuration by the thick disks. For median inclination angles, the radial velocity observed at infinity is larger because of both the rotation and radial motion of the fluid in the disk, which results in the luminous part of the images is much brighter. For larger inclination angles, such as the disk is edge on, the emission becomes dimmer at longer observational wavelengths (such as at 7.0mm and 3.5mm wavelengths), or brighter at shorter observational wavelengths (such as at 1.3 mm wavelength) than that of the face on case, except for the high spin and high inclination images. These complex behaviors are due to the combination of the Lorentz boosting effect and the radiative absorption in the disk. We hope our results are helpful to determine the spin parameter of the black hole in low luminosity sources, such as the Galactic center. A primary analysis by comparison with the observed sizes of Sgr A* at millimeters strongly suggests that the disk around the central black hole at Sgr A* is highly inclined or the central black hole is rotating fast.

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preprint2009arXiv

Polarized Emission of Sagittarius A*

We explore the parameter space of the two temperature pseudo-Newtonian Keplerian accretion flow model for the millimeter and shorter wavelength emission from Sagittarius A*. A general relativistic ray-tracing code is used to treat the radiative transfer of polarized synchrotron emission from the flow. The synchrotron self-Comptonization and bremsstrahlung emission components are also included. It is shown that the model can readily account for the millimeter to sub-millimeter emission characteristics with an accretion rate of ~6x10^17g.s^-1 and an inclination angle of ~40 deg. However, the corresponding model predicted near-infrared and X-ray fluxes are more than one order of magnitude lower than the observed 'quiescent' state values. While the extended quiescent-state X-ray emission has been attributed to thermal emission from the large-scale accretion flow, the NIR emission and flares are likely dominated by emission regions either within the last stable orbit of a Schwarzschild black hole or associated with outflows. With the viscous parameter derived from numerical simulations, there is still a degeneracy between the electron heating rate and the magnetic parameter. A fully general relativistic treatment with the black hole spin incorporated will resolve these issues.

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preprint2009arXiv

Testing the Accretion Flow with Plasma Wave Heating Mechanism for Sagittarius a* by the 1.3MM Vlbi Measurements

The vicinity of the supermassive black hole associated with the compact radio source Sagittarius (Sgr) A* is believed to dominate the observed emission at wavelengths near and shorter than $\sim$ 1 millimeter. We show that a general relativistic accretion flow, heated via the plasma wave heating mechanism, is consistent with the polarization and recent mm-VLBI observations of Sgr A* for an inclination angle of $\sim 45^\circ$, position angle of $\sim 140^\circ$, and spin $\lesssim 0.9$. Structure in visibilities produced by the black hole shadow can potentially be observed by 1.3 mm-VLBI on the existing Hawaii-CARMA and Hawaii-SMT baselines. We also consider eight additional potential mm-VLBI stations, including sites in Chile and New Zealand, finding that with these the basic geometry of the emission region can be reliably estimated.

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preprint2008arXiv

Switchable collective pinning of flux quanta using magnetic vortex arrays

We constructed a superconducting/ferromagnetic hybrid system in which the ordering of the pinning potential landscape for flux quanta can be manipulated. Flux pinning is induced by an array of magnetic nanodots in the magnetic vortex state, and controlled by the magnetic history. This allows switching on and off the collective pinning of the flux-lattice. In addition, we observed field-induced superconductivity that originates from the annihilation of flux quanta induced by the stray fields from the magnetic vortices.

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