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Tong Wu

Publications and source records attributed to Tong Wu.

At least 289 records · Page 16Linked to original sources
arXiv2021

Lens-free Optical Detection of Thermal Motion of a Sub-millimeter Sphere Diamagnetically Levitated in High Vacuum

Levitated oscillators with millimeter or sub-millimeter size are particularly attractive due to their potential role in studying various fundamental problems and practical applications. One of the crucial issues towards these goals is to achieve efficient measurements of oscillator motion, while this remains a challenge. Here we theoretically propose a lens-free optical detection scheme, which can be used to detect the motion of a millimeter or sub-millimeter levitated oscillator with a measurement efficiency close to the standard quantum limit with a modest optical power. We demonstrate experimentally this scheme on a 0.5 mm diameter micro-sphere that is diamagnetically levitated under high vacuum and room temperature, and the thermal motion is detected with high precision. Based on this system, an estimated acceleration sensitivity of $9.7 \times 10^{-10}\rm g/\sqrt{Hz}$ is achieved, which is more than one order improvement over the best value reported by the levitated mechanical system. Due to the stability of the system, the minimum resolved acceleration of $3.5\times 10^{-12}\rm g$ is reached with measurement times of $10^5$ s. This result is expected to have potential applications in the study of exotic interactions in the millimeter or sub-millimeter range and the realization of compact gravimeter and accelerometer.

quant-ph↗
arXiv2021

RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning

We introduce a new semi-supervised, time series anomaly detection algorithm that uses deep reinforcement learning (DRL) and active learning to efficiently learn and adapt to anomalies in real-world time series data. Our model - called RLAD - makes no assumption about the underlying mechanism that produces the observation sequence and continuously adapts the detection model based on experience with anomalous patterns. In addition, it requires no manual tuning of parameters and outperforms all state-of-art methods we compare with, both unsupervised and semi-supervised, across several figures of merit. More specifically, we outperform the best unsupervised approach by a factor of 1.58 on the F1 score, with only 1% of labels and up to around 4.4x on another real-world dataset with only 0.1% of labels. We compare RLAD with seven deep-learning based algorithms across two common anomaly detection datasets with up to around 3M data points and between 0.28% to 2.65% anomalies.We outperform all of them across several important performance metrics.

cs.LG↗
arXiv2021

Hot electron generation through near-field excitation of plasmonic nanoresonators

We theoretically study hot electron generation through the emission of a dipole source coupled to a nanoresonator on a metal surface. In our hybrid approach, we solve the time-harmonic Maxwell's equations numerically and apply a quantum model to predict the efficiency of hot electron generation. Strongly confined electromagnetic fields and the strong enhancement of hot electron generation at the metal surface are predicted and are further interpreted with the theory of quasinormal modes. In the investigated nanoresonator setup, both the emitting source and the acceptor resonator are localized in the same volume, and this configuration looks promising to achieve high efficiencies of hot electron generation. By comparing with the efficiency calculated in the absence of the plasmonic nanoresonator, that is, the dipole source is located near a flat, unstructured metal surface, we show that the effective excitation of the modes of the nanoresonator boosts the generation efficiency of energetic charge carriers. The proposed scheme can be used in tip-based spectroscopies and other optoelectronic applications.

physics.optics↗
arXiv2021

Searching spin-mass interaction using a diamagnetic levitated magnetic resonance force sensor

Axion-like particles (ALPs) are predicted to mediate exotic interactions between spin and mass. We propose an ALP-searching experiment based on the levitated micromechanical oscillator, which is one of the most sensitive sensors for spin-mass forces at a short distance. The proposed experiment tests the spin-mass resonant interaction between the polarized electron spins and a diamagnetically levitated microsphere. By periodically flipping the electron spins, the contamination from nonresonant background forces can be eliminated. The levitated microoscillator can prospectively enhance the sensitivity by nearly $10^3$ times over current experiments for ALPs with mass in the range 4 meV to 0.4 eV.

quant-ph↗
arXiv2021

A Revisit of Shape Editing Techniques: from the Geometric to the Neural Viewpoint

3D shape editing is widely used in a range of applications such as movie production, computer games and computer aided design. It is also a popular research topic in computer graphics and computer vision. In past decades, researchers have developed a series of editing methods to make the editing process faster, more robust, and more reliable. Traditionally, the deformed shape is determined by the optimal transformation and weights for an energy term. With increasing availability of 3D shapes on the Internet, data-driven methods were proposed to improve the editing results. More recently as the deep neural networks became popular, many deep learning based editing methods have been developed in this field, which is naturally data-driven. We mainly survey recent research works from the geometric viewpoint to those emerging neural deformation techniques and categorize them into organic shape editing methods and man-made model editing methods. Both traditional methods and recent neural network based methods are reviewed.

cs.GR↗
arXiv2020

Shaping Deep Feature Space towards Gaussian Mixture for Visual Classification

The softmax cross-entropy loss function has been widely used to train deep models for various tasks. In this work, we propose a Gaussian mixture (GM) loss function for deep neural networks for visual classification. Unlike the softmax cross-entropy loss, our method explicitly shapes the deep feature space towards a Gaussian Mixture distribution. With a classification margin and a likelihood regularization, the GM loss facilitates both high classification performance and accurate modeling of the feature distribution. The GM loss can be readily used to distinguish abnormal inputs, such as the adversarial examples, based on the discrepancy between feature distributions of the inputs and the training set. Furthermore, theoretical analysis shows that a symmetric feature space can be achieved by using the GM loss, which enables the models to perform robustly against adversarial attacks. The proposed model can be implemented easily and efficiently without using extra trainable parameters. Extensive evaluations demonstrate that the proposed method performs favorably not only on image classification but also on robust detection of adversarial examples generated by strong attacks under different threat models.

cs.CV↗
arXiv2020

Variability and Fidelity Limits of Silicon Quantum Gates Due to Random Interface Charge Traps

Silicon offers an attractive material platform for hardware realization of quantum computing. In this study, a microscopic stochastic simulation method is developed to model the effect of random interface charge traps in silicon metal-oxide-semiconductor (MOS) quantum gates. The statistical results show that by using a fast two-qubit gate in isotopically purified silicon, the two-qubit silicon-based quantum gates have the fidelity >98% with a probability of 75% for the state-of-the-art MOS interface quality. By using a composite gate pulse, the fidelity can be further improved to >99.5% with the 75% probability. The variations between the quantum gate devices, however, are largely due to the small number of traps per device. The results highlight the importance of variability consideration due to random charge traps and potential to improve fidelity in silicon-based quantum computing.

quant-ph↗
arXiv2020

Three-Dimensional Lip Motion Network for Text-Independent Speaker Recognition

Lip motion reflects behavior characteristics of speakers, and thus can be used as a new kind of biometrics in speaker recognition. In the literature, lots of works used two-dimensional (2D) lip images to recognize speaker in a textdependent context. However, 2D lip easily suffers from various face orientations. To this end, in this work, we present a novel end-to-end 3D lip motion Network (3LMNet) by utilizing the sentence-level 3D lip motion (S3DLM) to recognize speakers in both the text-independent and text-dependent contexts. A new regional feedback module (RFM) is proposed to obtain attentions in different lip regions. Besides, prior knowledge of lip motion is investigated to complement RFM, where landmark-level and frame-level features are merged to form a better feature representation. Moreover, we present two methods, i.e., coordinate transformation and face posture correction to pre-process the LSD-AV dataset, which contains 68 speakers and 146 sentences per speaker. The evaluation results on this dataset demonstrate that our proposed 3LMNet is superior to the baseline models, i.e., LSTM, VGG-16 and ResNet-34, and outperforms the state-of-the-art using 2D lip image as well as the 3D face. The code of this work is released at https://github.com/wutong18/Three-Dimensional-Lip- Motion-Network-for-Text-Independent-Speaker-Recognition.

cs.CV↗
arXiv2020

Electron-Backscattering-Assisted High Harmonic Generation from Bilayer Nanostructures

In the framework of time-dependent density functional theory, we obtain high-order harmonics of photon energies up to 10 Up from bilayer crystals with an interlayer spacing d = 70 Å. At grazing incidence, a clear double-plateau structure is observed in the harmonic spectrum. The photon energy of the second plateau far beyond atomic-like harmonics can be well explained by the inclusion of backscattering of ionized electrons. Ab initio simulations reveal that the cutoff of the second plateau is continuously extended with an increasing d. Our classical calculations predict that the maximum electronic kinetic energy is linearly dependent on d over a wide range. Moreover, the harmonic yield in the second plateau is significantly enhanced by increases in the wavelength of the driving laser. Owing to the confined spreading of the electronic wave packet, a beneficial wavelength scaling of λ2.85 is obtained. This study therefore establishes a novel and efficient way of producing high-energy light source based on layered nanostructures.

physics.optics↗
arXiv2020

Integrated Cooling (i-Cool) Textile of Heat Conduction and Sweat Transportation for Personal Perspiration Management

Perspiration evaporation plays an indispensable role in human body heat dissipation. However, conventional textiles show limited perspiration management capability in moderate/profuse perspiration scenarios, i.e. low evaporation ability, ineffective evaporative cooling effect, and resultant human body dehydration and electrolyte disorder. Here, we propose a novel concept of integrated cooling (i-Cool) textile of heat conduction and sweat transportation for personal perspiration management based on unique functional structure design. By integrating heat conductive pathways and water transport channels decently, this textile not only shows the capability of liquid water wicking, but also exhibits superior evaporation rate than traditional textiles. Furthermore, compared with cotton, about 2.8 $^\circ$C cooling effect causing less than one third amount of dehydration has also been demonstrated on the artificial sweating skin platform with feedback control loop simulating human body perspiration situation. Moreover, the practical application feasibility of the i-Cool textile design principles has been validated as well. Owing to its exceptional personal perspiration management performance in liquid water wicking, fast evaporation, efficient cooling effect and reduced human body dehydration/electrolyte loss, we expect this i-Cool textile provides promising design guidelines for next-generation personal perspiration management textile.

physics.app-ph↗
arXiv2020

Mechanical dissipation below 1$μ$Hz with a cryogenic diamagnetic-levitated micro-oscillator

Ultralow dissipation plays an important role in sensing applications and exploring macroscopic quantum phenomena using micro-and nano-mechanical systems. We report a diamagnetic-levitated micro-mechanical oscillator operating at a low temperature of 3K with measured dissipation as low as 0.59 $μ$Hz and a quality factor as high as $2 \times 10^7$. To the best of our knowledge the achieved dissipation is the lowest in micro- and nano-mechanical systems to date, orders of magnitude improvement over the reported state-of-the-art systems based on different principles. The cryogenic diamagnetic-levitated oscillator described here is applicable to a wide range of mass, making it a good candidate for measuring both force and acceleration with ultra-high sensitivity. By virtue of the naturally existing strong magnetic gradient, this system has great potential in quantum spin mechanics study.

quant-ph↗
arXiv2020

Physical Adversarial Attack on Vehicle Detector in the Carla Simulator

In this paper, we tackle the issue of physical adversarial examples for object detectors in the wild. Specifically, we proposed to generate adversarial patterns to be applied on vehicle surface so that it's not recognizable by detectors in the photo-realistic Carla simulator. Our approach contains two main techniques, an Enlarge-and-Repeat process and a Discrete Searching method, to craft mosaic-like adversarial vehicle textures without access to neither the model weight of the detector nor a differential rendering procedure. The experimental results demonstrate the effectiveness of our approach in the simulator.

cs.CV↗
arXiv2020

Human Action Attribute Learning From Video Data Using Low-Rank Representations

Representation of human actions as a sequence of human body movements or action attributes enables the development of models for human activity recognition and summarization. We present an extension of the low-rank representation (LRR) model, termed the clustering-aware structure-constrained low-rank representation (CS-LRR) model, for unsupervised learning of human action attributes from video data. Our model is based on the union-of-subspaces (UoS) framework, and integrates spectral clustering into the LRR optimization problem for better subspace clustering results. We lay out an efficient linear alternating direction method to solve the CS-LRR optimization problem. We also introduce a hierarchical subspace clustering approach, termed hierarchical CS-LRR, to learn the attributes without the need for a priori specification of their number. By visualizing and labeling these action attributes, the hierarchical model can be used to semantically summarize long video sequences of human actions at multiple resolutions. A human action or activity can also be uniquely represented as a sequence of transitions from one action attribute to another, which can then be used for human action recognition. We demonstrate the effectiveness of the proposed model for semantic summarization and action recognition through comprehensive experiments on five real-world human action datasets.

stat.ML↗
arXiv2020

Vector exceptional points with strong superchiral fields

Exceptional points(EPs), branch points of complex energy surfaces at which eigenvalues and eigenvectors coalesce, are ubiquitous in non-Hermitian systems. Many novel properties and applications have been proposed around the EPs. One of the important applications is to enhance the detection sensitivity. However, due to the lack of single-handed superchiral fields, all of the proposed EP-based sensing mechanisms are only useful for the non-chiral discrimination. Here, we propose theoretically and demonstrate experimentally a new type of EP, which is a called radiation vector EP, to fulfill the homogeneous superchiral fields for chiral sensing. This type of EP is realized by suitably tuning the coupling strength and radiation losses for a pair of orthogonal polarization modes in the photonic crystal slab. Based on the unique modal-coupling property at the vector EP, we demonstrate that the uniform superchiral fields can be generated with two beams of lights illuminating on the photonic crystal slab from opposite directions. Thus, the designed photonic crystal slab, which supports the vector EP, can be used to perform surface-enhanced chiral detection. Our findings provide a new strategy for ultrasensitive characterization and quantification of molecular chirality, a key aspect for various bioscience and biomedicine applications.

physics.optics↗
arXiv2020

Meta Segmentation Network for Ultra-Resolution Medical Images

Despite recent progress on semantic segmentation, there still exist huge challenges in medical ultra-resolution image segmentation. The methods based on multi-branch structure can make a good balance between computational burdens and segmentation accuracy. However, the fusion structure in these methods require to be designed elaborately to achieve desirable result, which leads to model redundancy. In this paper, we propose Meta Segmentation Network (MSN) to solve this challenging problem. With the help of meta-learning, the fusion module of MSN is quite simple but effective. MSN can fast generate the weights of fusion layers through a simple meta-learner, requiring only a few training samples and epochs to converge. In addition, to avoid learning all branches from scratch, we further introduce a particular weight sharing mechanism to realize a fast knowledge adaptation and share the weights among multiple branches, resulting in the performance improvement and significant parameters reduction. The experimental results on two challenging ultra-resolution medical datasets BACH and ISIC show that MSN achieves the best performance compared with the state-of-the-art methods.

cs.CV↗
arXiv2020

Defending Against Physically Realizable Attacks on Image Classification

We study the problem of defending deep neural network approaches for image classification from physically realizable attacks. First, we demonstrate that the two most scalable and effective methods for learning robust models, adversarial training with PGD attacks and randomized smoothing, exhibit very limited effectiveness against three of the highest profile physical attacks. Next, we propose a new abstract adversarial model, rectangular occlusion attacks, in which an adversary places a small adversarially crafted rectangle in an image, and develop two approaches for efficiently computing the resulting adversarial examples. Finally, we demonstrate that adversarial training using our new attack yields image classification models that exhibit high robustness against the physically realizable attacks we study, offering the first effective generic defense against such attacks.

cs.LG↗