Results for "J. C. Loy"

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Approximate amenability of tensor products of Banach algebrasApr 29 2016Jun 26 2016We show that the tensor product of approximately amenable algebras need not be approximately amenable, and investigate conditions under which $A$ and $B$ being approximately amenable implies, or is implied by, $A\hat{\otimes}B$ or $A^{\#}\hat{\otimes} ... More
Site-selective quantum control in an isotopically enriched 28Si/SiGe quadruple quantum dotMar 14 2019Silicon spin qubits are a promising quantum computing platform offering long coherence times, small device sizes, and compatibility with industry-backed device fabrication techniques. In recent years, high fidelity single-qubit and two-qubit operations ... More
Embracing Data ScienceJul 04 2016Statistics is running the risk of appearing irrelevant to today's undergraduate students. Today's undergraduate students are familiar with data science projects and they judge statistics against what they have seen. Statistics, especially at the introductory ... More
EDVR: Video Restoration with Enhanced Deformable Convolutional NetworksMay 07 2019Video restoration tasks, including super-resolution, deblurring, etc, are drawing increasing attention in the computer vision community. A challenging benchmark named REDS is released in the NTIRE19 Challenge. This new benchmark challenges existing methods ... More
Forbidden Subgraphs in Connected GraphsNov 25 2004Given a set $\xi=\{H_1,H_2,...\}$ of connected non acyclic graphs, a $\xi$-free graph is one which does not contain any member of $% \xi$ as copy. Define the excess of a graph as the difference between its number of edges and its number of vertices. Let ... More
Structure preserving schemes for nonlinear Fokker-Planck equations with anisotropic diffusionMay 08 2019In this work we propose novel numerical schemes for nonlinear Fokker-Planck-type equations with anisotropic diffusion matrix that preserve fundamental structural properties like non negativity of the solution, entropy dissipation and which guarantees ... More
Optimal Storage and Retrieval of Single-Photon WaveformsJul 11 2012We report an experimental demonstration of optimal storage and retrieval of heralded single-photon wave packets using electromagnetically induced transparency (EIT) in cold atoms at a high optical depth. We obtain an optimal storage efficiency of (49+/-3)% ... More
Variations of Q-Q Plots -- The Power of our Eyes!Mar 06 2015In statistical modeling we strive to specify models that resemble data collected in studies or observed from processes. Consequently, distributional specification and parameter estimation are central to parametric models. Graphical procedures, such as ... More
Local Similarity-Aware Deep Feature EmbeddingOct 27 2016Existing deep embedding methods in vision tasks are capable of learning a compact Euclidean space from images, where Euclidean distances correspond to a similarity metric. To make learning more effective and efficient, hard sample mining is usually employed, ... More
Better Diagnostics for Linear Mixed-Effects Models Using Visual InferenceFeb 24 2015Oct 09 2015Linear mixed-effects (LME) models are versatile models that account for dependence structures when data are composed of groups. The additional flexibility of random effects models comes at the cost of complicating model exploration and validation due ... More
Discriminative Sparse Neighbor Approximation for Imbalanced LearningFeb 03 2016Data imbalance is common in many vision tasks where one or more classes are rare. Without addressing this issue conventional methods tend to be biased toward the majority class with poor predictive accuracy for the minority class. These methods further ... More
Dense Intrinsic Appearance Flow for Human Pose TransferMar 27 2019We present a novel approach for the task of human pose transfer, which aims at synthesizing a new image of a person from an input image of that person and a target pose. We address the issues of limited correspondences identified between keypoints only ... More
Aesthetic-Driven Image Enhancement by Adversarial LearningJul 17 2017Jul 02 2018We introduce EnhanceGAN, an adversarial learning based model that performs automatic image enhancement. Traditional image enhancement frameworks typically involve training models in a fully-supervised manner, which require expensive annotations in the ... More
Image Aesthetic Assessment: An Experimental SurveyOct 04 2016This survey aims at reviewing recent techniques used in the assessment of image aesthetic quality. The assessment of image aesthetic quality is the process of computationally distinguishing high-quality photos from low-quality ones based on photographic ... More
Accelerating the Super-Resolution Convolutional Neural NetworkAug 01 2016As a successful deep model applied in image super-resolution (SR), the Super-Resolution Convolutional Neural Network (SRCNN) has demonstrated superior performance to the previous hand-crafted models either in speed and restoration quality. However, the ... More
Learning from Multiple Sources for Video SummarisationJan 13 2015Feb 06 2015Many visual surveillance tasks, e.g.video summarisation, is conventionally accomplished through analysing imagerybased features. Relying solely on visual cues for public surveillance video understanding is unreliable, since visual observations obtained ... More
Instance-level Facial Attributes Transfer with Geometry-Aware FlowNov 30 2018We address the problem of instance-level facial attribute transfer without paired training data, e.g. faithfully transferring the exact mustache from a source face to a target face. This is a more challenging task than the conventional semantic-level ... More
Image Aesthetic Assessment: An Experimental SurveyOct 04 2016Apr 20 2017This survey aims at reviewing recent computer vision techniques used in the assessment of image aesthetic quality. Image aesthetic assessment aims at computationally distinguishing high-quality photos from low-quality ones based on photographic rules, ... More
Image Super-Resolution Using Deep Convolutional NetworksDec 31 2014Jul 31 2015We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping between the low/high-resolution images. The mapping is represented as a deep convolutional neural network (CNN) that takes the low-resolution ... More
Face Detection through Scale-Friendly Deep Convolutional NetworksJun 09 2017In this paper, we share our experience in designing a convolutional network-based face detector that could handle faces of an extremely wide range of scales. We show that faces with different scales can be modeled through a specialized set of deep convolutional ... More
A Flexible Modeling Approach for Robust Multi-Lane Road EstimationJun 06 2017A robust estimation of road course and traffic lanes is an essential part of environment perception for next generations of Advanced Driver Assistance Systems and development of self-driving vehicles. In this paper, a flexible method for modeling multiple ... More
Prime Sample Attention in Object DetectionApr 09 2019It is a common paradigm in object detection frameworks to treat all samples equally and target at maximizing the performance on average. In this work, we revisit this paradigm through a careful study on how different samples contribute to the overall ... More
WIDER FACE: A Face Detection BenchmarkNov 20 2015Face detection is one of the most studied topics in the computer vision community. Much of the progresses have been made by the availability of face detection benchmark datasets. We show that there is a gap between current face detection performance and ... More
Learning Social Relation Traits from Face ImagesSep 14 2015Social relation defines the association, e.g, warm, friendliness, and dominance, between two or more people. Motivated by psychological studies, we investigate if such fine-grained and high-level relation traits can be characterised and quantified from ... More
Learning Deep Representation for Face Alignment with Auxiliary AttributesAug 18 2014Aug 11 2015In this study, we show that landmark detection or face alignment task is not a single and independent problem. Instead, its robustness can be greatly improved with auxiliary information. Specifically, we jointly optimize landmark detection together with ... More
From Facial Expression Recognition to Interpersonal Relation PredictionSep 21 2016Sep 22 2016Interpersonal relation defines the association, e.g., warm, friendliness, and dominance, between two or more people. Motivated by psychological studies, we investigate if such fine-grained and high-level relation traits can be characterized and quantified ... More
Learning to Recognize Pedestrian AttributeJan 05 2015Apr 29 2015Learning to recognize pedestrian attributes at far distance is a challenging problem in visual surveillance since face and body close-shots are hardly available; instead, only far-view image frames of pedestrian are given. In this study, we present an ... More
Learning to Disambiguate by Asking Discriminative QuestionsAug 09 2017The ability to ask questions is a powerful tool to gather information in order to learn about the world and resolve ambiguities. In this paper, we explore a novel problem of generating discriminative questions to help disambiguate visual instances. Our ... More
Towards Arbitrary-View Face Alignment by Recommendation TreesNov 20 2015Learning to simultaneously handle face alignment of arbitrary views, e.g. frontal and profile views, appears to be more challenging than we thought. The difficulties lay in i) accommodating the complex appearance-shape relations exhibited in different ... More
A Large-Scale Car Dataset for Fine-Grained Categorization and VerificationJun 30 2015Sep 24 2015Updated on 24/09/2015: This update provides preliminary experiment results for fine-grained classification on the surveillance data of CompCars. The train/test splits are provided in the updated dataset. See details in Section 6.
Deep Imbalanced Learning for Face Recognition and Attribute PredictionJun 01 2018Data for face analysis often exhibit highly-skewed class distribution, i.e., most data belong to a few majority classes, while the minority classes only contain a scarce amount of instances. To mitigate this issue, contemporary deep learning methods typically ... More
Deep Imbalanced Learning for Face Recognition and Attribute PredictionJun 01 2018Apr 30 2019Data for face analysis often exhibit highly-skewed class distribution, i.e., most data belong to a few majority classes, while the minority classes only contain a scarce amount of instances. To mitigate this issue, contemporary deep learning methods typically ... More
Crafting a Toolchain for Image Restoration by Deep Reinforcement LearningApr 10 2018We investigate a novel approach for image restoration by reinforcement learning. Unlike existing studies that mostly train a single large network for a specialized task, we prepare a toolbox consisting of small-scale convolutional networks of different ... More
Deep Flow-Guided Video InpaintingMay 08 2019Video inpainting, which aims at filling in missing regions of a video, remains challenging due to the difficulty of preserving the precise spatial and temporal coherence of video contents. In this work we propose a novel flow-guided video inpainting approach. ... More
Deep Cascaded Bi-Network for Face HallucinationJul 18 2016We present a novel framework for hallucinating faces of unconstrained poses and with very low resolution (face size as small as 5pxIOD). In contrast to existing studies that mostly ignore or assume pre-aligned face spatial configuration (e.g. facial landmarks ... More
PolyNet: A Pursuit of Structural Diversity in Very Deep NetworksNov 17 2016A number of studies have shown that increasing the depth or width of convolutional networks is a rewarding approach to improve the performance of image recognition. In our study, however, we observed difficulties along both directions. On one hand, the ... More
Multi-Lane Perception Using Feature Fusion Based on GraphSLAMJun 14 2017An extensive, precise and robust recognition and modeling of the environment is a key factor for next generations of Advanced Driver Assistance Systems and development of autonomous vehicles. In this paper, a real-time approach for the perception of multiple ... More
Training Competitive Binary Neural Networks from ScratchDec 05 2018Convolutional neural networks have achieved astonishing results in different application areas. Various methods that allow us to use these models on mobile and embedded devices have been proposed. Especially binary neural networks are a promising approach ... More
An Empirical Study of Recent Face Alignment MethodsNov 16 2015The problem of face alignment has been intensively studied in the past years. A large number of novel methods have been proposed and reported very good performance on benchmark dataset such as 300W. However, the differences in the experimental setting ... More
Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature TransformApr 09 2018Despite that convolutional neural networks (CNN) have recently demonstrated high-quality reconstruction for single-image super-resolution (SR), recovering natural and realistic texture remains a challenging problem. In this paper, we show that it is possible ... More
Learning to Steer by Mimicking Features from Heterogeneous Auxiliary NetworksNov 07 2018The training of many existing end-to-end steering angle prediction models heavily relies on steering angles as the supervisory signal. Without learning from much richer contexts, these methods are susceptible to the presence of sharp road curves, challenging ... More
Transferring Landmark Annotations for Cross-Dataset Face AlignmentSep 02 2014Dataset bias is a well known problem in object recognition domain. This issue, nonetheless, is rarely explored in face alignment research. In this study, we show that dataset plays an integral part of face alignment performance. Specifically, owing to ... More
From Facial Parts Responses to Face Detection: A Deep Learning ApproachSep 22 2015In this paper, we propose a novel deep convolutional network (DCN) that achieves outstanding performance on FDDB, PASCAL Face, and AFW. Specifically, our method achieves a high recall rate of 90.99% on the challenging FDDB benchmark, outperforming the ... More
Quantifying Facial Age by Posterior of Age ComparisonsAug 31 2017Oct 13 2017We introduce a novel approach for annotating large quantity of in-the-wild facial images with high-quality posterior age distribution as labels. Each posterior provides a probability distribution of estimated ages for a face. Our approach is motivated ... More
A Lightweight Optical Flow CNN - Revisiting Data Fidelity and RegularizationMar 15 2019Over four decades, the majority addresses the problem of optical flow estimation using variational methods. With the advance of machine learning, some recent works have attempted to address the problem using convolutional neural network (CNN) and have ... More
Deep Convolution Networks for Compression Artifacts ReductionAug 09 2016Lossy compression introduces complex compression artifacts, particularly blocking artifacts, ringing effects and blurring. Existing algorithms either focus on removing blocking artifacts and produce blurred output, or restore sharpened images that are ... More
Backreaction of fermionic perturbations in the Hamiltonian of hybrid loop quantum cosmologyMay 10 2018Oct 01 2018We discuss the freedom available in hybrid loop quantum cosmology to define canonical variables for the matter content and investigate whether this can be used to derive a quantum field theory with good properties for the matter sector. We study a primordial, ... More
Compression Artifacts Reduction by a Deep Convolutional NetworkApr 27 2015Lossy compression introduces complex compression artifacts, particularly the blocking artifacts, ringing effects and blurring. Existing algorithms either focus on removing blocking artifacts and produce blurred output, or restores sharpened images that ... More
Discover and Learn New Objects from DocumentariesJul 30 2017Despite the remarkable progress in recent years, detecting objects in a new context remains a challenging task. Detectors learned from a public dataset can only work with a fixed list of categories, while training from scratch usually requires a large ... More
Merge or Not? Learning to Group Faces via Imitation LearningJul 13 2017Given a large number of unlabeled face images, face grouping aims at clustering the images into individual identities present in the data. This task remains a challenging problem despite the remarkable capability of deep learning approaches in learning ... More
Narrowband Biphotons with Polarization-Frequency Coupled HyperentanglementNov 22 2014Dec 12 2014We demonstrate the generation of narrowband biphotons with polarization-frequency coupled hy- perentanglement from spontaneous four-wave mixing in cold atoms. The coupling between polariza- tion and frequency is realized through a frequency shifter and ... More
An integrated single- and two-photon non-diffracting light-sheet microscopeDec 07 2017Dec 18 2017We describe the apparatus of a fluorescence optical microscope with both single-photon and two-photon non-diffracting light sheets excitation for large volume imaging. With special design to accommodate two different wavelength ranges (visible: 400-700 ... More
Optimizing Video Object Detection via a Scale-Time LatticeApr 16 2018High-performance object detection relies on expensive convolutional networks to compute features, often leading to significant challenges in applications, e.g. those that require detecting objects from video streams in real time. The key to this problem ... More
The Devil of Face Recognition is in the NoiseJul 31 2018The growing scale of face recognition datasets empowers us to train strong convolutional networks for face recognition. While a variety of architectures and loss functions have been devised, we still have a limited understanding of the source and consequence ... More
Deep Learning Markov Random Field for Semantic SegmentationJun 23 2016Semantic segmentation tasks can be well modeled by Markov Random Field (MRF). This paper addresses semantic segmentation by incorporating high-order relations and mixture of label contexts into MRF. Unlike previous works that optimized MRFs using iterative ... More
Path-Restore: Learning Network Path Selection for Image RestorationApr 23 2019Very deep Convolutional Neural Networks (CNNs) have greatly improved the performance on various image restoration tasks. However, this comes at a price of increasing computational burden, which limits their practical usages. We believe that some corrupted ... More
Unsupervised Bi-directional Flow-based Video Generation from one SnapshotMar 03 2019Imagining multiple consecutive frames given one single snapshot is challenging, since it is difficult to simultaneously predict diverse motions from a single image and faithfully generate novel frames without visual distortions. In this work, we leverage ... More
Disentangling Content and Style via Unsupervised Geometry DistillationMay 11 2019It is challenging to disentangle an object into two orthogonal spaces of content and style since each can influence the visual observation differently and unpredictably. It is rare for one to have access to a large number of data to help separate the ... More
TransGaGa: Geometry-Aware Unsupervised Image-to-Image TranslationApr 21 2019Unsupervised image-to-image translation aims at learning a mapping between two visual domains. However, learning a translation across large geometry variations always ends up with failure. In this work, we present a novel disentangle-and-translate framework ... More
Semantic Image Segmentation via Deep Parsing NetworkSep 09 2015Sep 24 2015This paper addresses semantic image segmentation by incorporating rich information into Markov Random Field (MRF), including high-order relations and mixture of label contexts. Unlike previous works that optimized MRFs using iterative algorithm, we solve ... More
Self-Supervised Learning via Conditional Motion PropagationMar 27 2019Intelligent agent naturally learns from motion. Various self-supervised algorithms have leveraged motion cues to learn effective visual representations. The hurdle here is that motion is both ambiguous and complex, rendering previous works either suffer ... More
Feature Matters: A Stage-by-Stage Approach for Knowledge TransferDec 05 2018Convolutional Neural Networks (CNNs) become deeper and deeper in recent years, making the study of model acceleration imperative. It is a common practice to employ a shallow network, called student, to learn from a deep one, which is termed as teacher. ... More
Boosting Optical Character Recognition: A Super-Resolution ApproachJun 07 2015Text image super-resolution is a challenging yet open research problem in the computer vision community. In particular, low-resolution images hamper the performance of typical optical character recognition (OCR) systems. In this article, we summarize ... More
Improving On-policy Learning with Statistical Reward AccumulationSep 07 2018Deep reinforcement learning has obtained significant breakthroughs in recent years. Most methods in deep-RL achieve good results via the maximization of the reward signal provided by the environment, typically in the form of discounted cumulative returns. ... More
CARAFE: Content-Aware ReAssembly of FEaturesMay 06 2019Feature upsampling is a key operation in a number of modern convolutional network architectures, e.g. feature pyramids. Its design is critical for dense prediction tasks such as object detection and semantic/instance segmentation. In this work, we propose ... More
Zoom-Net: Mining Deep Feature Interactions for Visual Relationship RecognitionJul 13 2018Recognizing visual relationships <subject-predicate-object> among any pair of localized objects is pivotal for image understanding. Previous studies have shown remarkable progress in exploiting linguistic priors or external textual information to improve ... More
Self-Supervised Learning via Conditional Motion PropagationMar 27 2019Apr 03 2019Intelligent agent naturally learns from motion. Various self-supervised algorithms have leveraged motion cues to learn effective visual representations. The hurdle here is that motion is both ambiguous and complex, rendering previous works either suffer ... More
Region Proposal by Guided AnchoringJan 10 2019Region anchors are the cornerstone of modern object detection techniques. State-of-the-art detectors mostly rely on a dense anchoring scheme, where anchors are sampled uniformly over the spatial domain with a predefined set of scales and aspect ratios. ... More
Learning to Cluster Faces on an Affinity GraphApr 04 2019Face recognition sees remarkable progress in recent years, and its performance has reached a very high level. Taking it to a next level requires substantially larger data, which would involve prohibitive annotation cost. Hence, exploiting unlabeled data ... More
Be Your Own Prada: Fashion Synthesis with Structural CoherenceOct 19 2017We present a novel and effective approach for generating new clothing on a wearer through generative adversarial learning. Given an input image of a person and a sentence describing a different outfit, our model "redresses" the person as desired, while ... More
Deep Network Interpolation for Continuous Imagery Effect TransitionNov 26 2018Deep convolutional neural network has demonstrated its capability of learning a deterministic mapping for the desired imagery effect. However, the large variety of user flavors motivates the possibility of continuous transition among different output ... More
Crowd Saliency Detection via Global Similarity StructureOct 14 2014It is common for CCTV operators to overlook inter- esting events taking place within the crowd due to large number of people in the crowded scene (i.e. marathon, rally). Thus, there is a dire need to automate the detection of salient crowd regions acquiring ... More
Region Proposal by Guided AnchoringJan 10 2019Apr 12 2019Region anchors are the cornerstone of modern object detection techniques. State-of-the-art detectors mostly rely on a dense anchoring scheme, where anchors are sampled uniformly over the spatial domain with a predefined set of scales and aspect ratios. ... More
Consensus-Driven Propagation in Massive Unlabeled Data for Face RecognitionSep 05 2018Jan 01 2019Face recognition has witnessed great progress in recent years, mainly attributed to the high-capacity model designed and the abundant labeled data collected. However, it becomes more and more prohibitive to scale up the current million-level identity ... More
CARAFE: Content-Aware ReAssembly of FEaturesMay 06 2019May 07 2019Feature upsampling is a key operation in a number of modern convolutional network architectures, e.g. feature pyramids. Its design is critical for dense prediction tasks such as object detection and semantic/instance segmentation. In this work, we propose ... More
Frequency-Bin Entanglement with Tunable PhaseApr 15 2015We describe a technique to produce narrow-band photon pairs with frequency-bin entanglement, whose relative phase can be tuned using linear polarization optics. We show that, making use of the polarization-frequency coupling effect, the phase of a complex ... More
Temporal Quantum-State Tomography of Narrowband BiphotonsSep 19 2014We describe and demonstrate a quantum state tomography for measuring the complex temporal waveform of narrowband biphotons. Through six sets of two-photon interference measurements projected in different polarization subspaces, we can construct the time-frequency ... More
Robust and Fast Decoding of High-Capacity Color QR Codes for Mobile ApplicationsApr 21 2017May 19 2018The use of color in QR codes brings extra data capacity, but also inflicts tremendous challenges on the decoding process due to chromatic distortion, cross-channel color interference and illumination variation. Particularly, we further discover a new ... More
Reading Scene Text in Deep Convolutional SequencesJun 14 2015Dec 20 2015We develop a Deep-Text Recurrent Network (DTRN) that regards scene text reading as a sequence labelling problem. We leverage recent advances of deep convolutional neural networks to generate an ordered high-level sequence from a whole word image, avoiding ... More
Pose-Robust Face Recognition via Deep Residual Equivariant MappingMar 02 2018Face recognition achieves exceptional success thanks to the emergence of deep learning. However, many contemporary face recognition models still perform relatively poor in processing profile faces compared to frontal faces. A key reason is that the number ... More
ReenactGAN: Learning to Reenact Faces via Boundary TransferJul 29 2018We present a novel learning-based framework for face reenactment. The proposed method, known as ReenactGAN, is capable of transferring facial movements and expressions from monocular video input of an arbitrary person to a target person. Instead of performing ... More
Non-Local Recurrent Network for Image RestorationJun 07 2018Dec 11 2018Many classic methods have shown non-local self-similarity in natural images to be an effective prior for image restoration. However, it remains unclear and challenging to make use of this intrinsic property via deep networks. In this paper, we propose ... More
Self-Supervised Learning via Conditional Motion PropagationMar 27 2019Apr 25 2019Intelligent agent naturally learns from motion. Various self-supervised algorithms have leveraged motion cues to learn effective visual representations. The hurdle here is that motion is both ambiguous and complex, rendering previous works either suffer ... More
Learning to Cluster Faces on an Affinity GraphApr 04 2019May 05 2019Face recognition sees remarkable progress in recent years, and its performance has reached a very high level. Taking it to a next level requires substantially larger data, which would involve prohibitive annotation cost. Hence, exploiting unlabeled data ... More
Mix-and-Match Tuning for Self-Supervised Semantic SegmentationDec 02 2017Jan 30 2018Deep convolutional networks for semantic image segmentation typically require large-scale labeled data, e.g. ImageNet and MS COCO, for network pre-training. To reduce annotation efforts, self-supervised semantic segmentation is recently proposed to pre-train ... More
A Scheme for Ultrasensitive Detection of Molecules by Using Vibrational Spectroscopy in Combination with Signal ProcessingDec 05 2018Dec 07 2018We show that combining vibrational spectroscopy with signal processing can result in a scheme for ultrasensitive detection of molecules. We consider the vibrational spectrum as a signal on the energy axis and apply a matched filter on that axis. On the ... More
Video Object Segmentation with Re-identificationAug 01 2017Conventional video segmentation methods often rely on temporal continuity to propagate masks. Such an assumption suffers from issues like drifting and inability to handle large displacement. To overcome these issues, we formulate an effective mechanism ... More
ESRGAN: Enhanced Super-Resolution Generative Adversarial NetworksSep 01 2018Sep 17 2018The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied with unpleasant artifacts. To ... More
Infrared dynamic polarizability of HD+ rovibrational statesJun 07 2011A calculation of dynamic polarizabilities of rovibrational states with vibrational quantum number $v=0-7$ and rotational quantum number $J=0,1$ in the 1s$\sigma_g$ ground-state potential of HD$^+$ is presented. Polarizability contributions by transitions ... More
Subnatural-linewidth biphotons from a Doppler-broadened hot atomic vapor cellFeb 27 2016We report the efficient generation of subnatural-linewidth (< 6 MHz, for Rb D1/D2 lines) biphotons from a Doppler-broadened (530 MHz) hot atomic vapor cell. We use on-resonance spontaneous four-wave mixing in a hot paraffin-coated 87Rb vapor cell at 63 ... More
Progress in structure recovery from low dose exposures: Mixed molecular adsorption, exploitation of symmetry and reconstruction from the minimum signal levelMar 26 2018We investigate the recovery of structures from large-area, low dose exposures that distribute the dose over many identical copies of an object. The reconstruction is done via a maximum likelihood approach that does neither require to identify nor align ... More
Colloidal Dynamics on Disordered SubstratesNov 15 2001Using Langevin simulations we examine driven colloids interacting with quenched disorder. For weak substrates the colloids form an ordered state and depin elastically. For increasing substrate strength we find a sharp crossover to inhomogeneous depinning ... More
Vortex Pinball Under Crossed AC Drives in Superconductors with Periodic Pinning ArraysNov 01 2001Vortices driven with both a transverse and a longitudinal AC drive which are out of phase are shown to exhibit a novel commensuration-incommensuration effect when interacting with periodic substrates. For different AC driving parameters, the motion of ... More
Transverse Phase Locking for Vortex Motion in Square and Triangular Pinning ArraysJan 03 2002We analyze transverse phase locking for vortex motion in a superconductor with a longitudinal DC drive and a transverse AC drive. For both square and triangular arrays we observe a variety of fractional phase locking steps in the velocity versus DC drive ... More
IST versus PDE, a comparative studySep 06 2014Sep 09 2014We survey and compare, mainly in the two-dimensional case, various results obtained by IST and PDE techniques for integrable equations. We also comment on what can be predicted from integrable equations on non integrable ones.
Transverse depinning in strongly driven vortex lattices with disorderOct 02 1999Using numerical simulations we investigate the transverse depinning of moving vortex lattices interacting with random disorder. We observe a finite transverse depinning barrier for vortex lattices that are driven with high longitudinal drives, when the ... More
On finite range stable type concentrationDec 16 2004The purpose of these notes is to further complete our understanding of the stable concentration phenomenon, by obtaining the finite range behavior of $P(F-E[F]\geq x)$, with $F=f(X)$ where $f$ is a Lipschitz function and $X$ is a stable random vector ... More
Dynamic Ordering and Transverse Depinning of a Driven Elastic String in a Disordered MediaMar 05 2001We examine the dynamics of an elastic string interacting with quenched disorder driven perpendicular and parallel to the string. We show that the string is the most disordered at the depinning transition but with increasing drive partial ordering is regained. ... More
Determining the cosmic ray ionization rate in dynamically evolving cloudsNov 02 2005The ionization fraction is an important factor in determining the chemical and physical evolution of star forming regions. In the dense, dark starless cores of such objects, the ionization rate is dominated by cosmic rays; it is therefore possible to ... More
Complex oscillator and Painlevé IV equationMar 27 2015Supersymmetric quantum mechanics is a powerful tool for generating exactly solvable potentials departing from a given initial one. In this article the first- and second- order supersymmetric transformations will be used to obtain new exactly solvable ... More