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Pei Yu

Publications and source records attributed to Pei Yu.

At least 37 records · Page 2Linked to original sources
arXiv2023

Nilpotent center conditions in cubic switching polynomial Liénard systems by higher-order analysis

The aim of this paper is to investigate two classical problems related to nilpotent center conditions and bifurcation of limit cycles in switching polynomial systems. Due to the difficulty in calculating the Lyapunov constants of switching polynomial systems at non-elementary singular points, it is extremely difficult to use the existing Poincaré-Lyapunov method to study these two problems. In this paper, we develop a higher-order Poincaré-Lyapunov method to consider the nilpotent center problem in switching polynomial systems, with particular attention focused on cubic switching Liénard systems. With proper perturbations, explicit center conditions are derived for switching Liénard systems at a nilpotent center, which is characterized as global. Moreover, with Bogdanov-Takens bifurcation theory, the existence of five limit cycles around the nilpotent center is proved for a class of switching Liénard systems, which is a new lower bound of cyclicity for such polynomial systems around a nilpotent center.

math.DS↗
arXiv2023

Imaging magnetism evolution of magnetite to megabar pressure range with quantum sensors in diamond anvil cell

High-pressure diamond anvil cells have been widely used to create novel states of matter. Nevertheless, the lack of universal in-situ magnetic measurement techniques at megabar pressures makes it difficult to understand the underlying physics of materials' behavior at extreme conditions, such as high-temperature superconductivity of hydrides and the formation or destruction of the local magnetic moments in magnetic systems, etc. Here we break through the limitations of pressure on quantum sensors and develop the in-situ magnetic detection technique at megabar pressures with high sensitivity (~1μT/Hz^(1\2)) and sub-microscale spatial resolution. By directly imaging the magnetic field and the evolution of magnetic domains, we observe the macroscopic magnetic transition of Fe3O4 in the megabar pressure range from strong ferromagnetism (α-Fe3O4) to weak ferromagnetism (β-Fe3O4) and finally to non-magnetism (γ-Fe3O4). The scenarios for magnetic changes in Fe3O4 characterized here shed light on the direct magnetic microstructure observation in bulk materials at high pressure and contribute to understanding the mechanism of magnetic moment suppression related to spin crossover. The presented method can potentially investigate the spin-orbital coupling and magnetism-superconductivity competition in magnetic systems.

physics.app-ph↗
arXiv2023

Sensing orbital hybridization of graphene-diamond interface with a single spin

Interfacial interactions are crucial in a variety of fields and can greatly affect the electric, magnetic, and chemical properties of materials. Among them, interface orbital hybridization plays a fundamental role in the properties of surface electrons such as dispersion, interaction, and ground states. Conventional measurements of electronic states at interfaces such as scanning tunneling microscopes are all based on electric interactions which, however, suffer from strong perturbation on these electrons. Here we unveil a new experimental detection of interface electrons based on the weak magnetic interactions between them and the nitrogen-vacancy (NV) center in diamond. With negligible perturbation on the interface electrons, their physical properties can be revealed by the NV spin coherence time. In our system, the interface interaction leads to significant decreases in both the density and coherence time of the electron spins at the diamond-graphene interface. Furthermore, together with electron spin resonance spectra and first-principle calculations, we can retrieve the effect of interface electron orbital hybridization. Our study opens a new pathway toward the microscopic probing of interfacial electronic states with weak magnetic interactions and provides a new avenue for future research on material interfaces.

quant-ph↗
arXiv2023

A Token-level Contrastive Framework for Sign Language Translation

Sign Language Translation (SLT) is a promising technology to bridge the communication gap between the deaf and the hearing people. Recently, researchers have adopted Neural Machine Translation (NMT) methods, which usually require large-scale corpus for training, to achieve SLT. However, the publicly available SLT corpus is very limited, which causes the collapse of the token representations and the inaccuracy of the generated tokens. To alleviate this issue, we propose ConSLT, a novel token-level \textbf{Con}trastive learning framework for \textbf{S}ign \textbf{L}anguage \textbf{T}ranslation , which learns effective token representations by incorporating token-level contrastive learning into the SLT decoding process. Concretely, ConSLT treats each token and its counterpart generated by different dropout masks as positive pairs during decoding, and then randomly samples $K$ tokens in the vocabulary that are not in the current sentence to construct negative examples. We conduct comprehensive experiments on two benchmarks (PHOENIX14T and CSL-Daily) for both end-to-end and cascaded settings. The experimental results demonstrate that ConSLT can achieve better translation quality than the strong baselines.

cs.CL↗
arXiv2022

Sub-nanotesla Sensitivity at the Nanoscale with a Single Spin

High-sensitivity detection of microscopic magnetic field is essential in many fields. Good sensitivity and high spatial resolution are mutually contradictory in measurement, which is quantified by the energy resolution limit (ERL). Here we report that a sensitivity of 0.5 ${\bf{nT/\sqrt{Hz}}}$ at the nanoscale is achieved experimentally by using nitrogen-vacancy defects in diamond with depths of tens of nanometers. The achieved sensitivity is substantially enhanced by integrating with multiple quantum techniques, including real-time-feedback initialization, dynamical decoupling with shaped pulses, repetitive readout via quantum logic. Our magnetic sensors will shed new light on searching new physics beyond the standard model, investigating microscopic magnetic phenomena in condensed matters, and detection of life activities at the sub-cellular scale.

quant-ph↗
arXiv2022

Temperature-dependent behaviors of single spin defects in solids determined with Hz-level precision

Revealing the properties of single spin defects in solids is essential for quantum applications based on solid-state systems. However, it is intractable to investigate the temperature-dependent properties of single defects, due to the low precision for single-defect measurements in contrast to defect ensembles. Here we report that the temperature dependence of the Hamiltonian parameters for single negatively charged nitrogen-vacancy (NV$^{-}$) centers in diamond is precisely measured, and the results find a reasonable agreement with first-principles calculations. Particularly, the hyperfine interactions with randomly distributed $^{13}$C nuclear spins are clearly observed to vary with temperature, and the relevant coefficients are measured with Hz-level precision. The temperature-dependent behaviors are attributed to both thermal expansion and lattice vibrations by first-principles calculations. Our results pave the way for taking nuclear spins as more stable thermometers at nanoscale. The methods developed here for high-precision measurements and first-principles calculations can be further extended to other solid-state spin defects.

quant-ph↗
arXiv2022

Observation of uniaxial strain tuned spin cycloid in a freestanding BiFeO3 film

Non-collinear spin order that breaks space inversion symmetry and allows efficient electric-field control of magnetism makes BiFeO$_3$ a promising candidate for applications in low-power spintronic devices. Epitaxial strain effects have been intensively studied and exhibit significant modulation of the magnetic order in BiFeO$_3$, but tuning its spin structure with continuously varied uniaxial strain is still lacking up to date. Here, we apply \emph{in situ} uniaxial strain to a freestanding BiFeO$_3$ film and use scanning NV microscope to image the nanoscale magnetic order in real-space. The strain is continuously increased from 0\% to 1.5\% and four images under different strains are acquired during this period. The images show that the spin cycloid tilts by $\sim 12.6^\circ$ when strain approaches 1.5\%. A first principle calculation has been processed to show that the tilting is energetically favorable under such strain. Our \emph{in situ} strain applying method in combination with scanning NV microscope real-space imaging ability paves a new way in studying the coupling between magnetic order and strain in BiFeO$_3$ films.

cond-mat.mtrl-sci↗
arXiv2022

Picotesla magnetometry of microwave fields with diamond sensors

Developing robust microwave-field sensors is both fundamentally and practically important with a wide range of applications from astronomy to communication engineering. The Nitrogen-Vacancy (NV) center in diamond is an attractive candidate for such purpose because of its magnetometric sensitivity, stability and compatibility with ambient conditions. However, the existing NV center-based magnetometers have limited sensitivity in the microwave band. Here we present a continuous heterodyne detection method that can enhance the sensor's response to weak microwaves, even in the absence of spin controls. Experimentally, we achieve a sensitivity of 8.9 pT$\cdot$Hz$^{-1/2}$ for microwaves of 2.9 GHz by simultaneously using an ensemble of $n_{\text{NV}} \sim 2.8\times10^{13}$ NV centers within a sensor volume of $4\times10^{-2}$ mm$^3$. Besides, we also achieve $1/t$ scaling of frequency resolution up to measurement time $t$ of 10000 s. Our method removes the control pulses and thus will greatly benefit the practical application of diamond-based microwave sensors.

quant-ph↗
arXiv2022

Should All Proposals be Treated Equally in Object Detection?

The complexity-precision trade-off of an object detector is a critical problem for resource constrained vision tasks. Previous works have emphasized detectors implemented with efficient backbones. The impact on this trade-off of proposal processing by the detection head is investigated in this work. It is hypothesized that improved detection efficiency requires a paradigm shift, towards the unequal processing of proposals, assigning more computation to good proposals than poor ones. This results in better utilization of available computational budget, enabling higher accuracy for the same FLOPS. We formulate this as a learning problem where the goal is to assign operators to proposals, in the detection head, so that the total computational cost is constrained and the precision is maximized. The key finding is that such matching can be learned as a function that maps each proposal embedding into a one-hot code over operators. While this function induces a complex dynamic network routing mechanism, it can be implemented by a simple MLP and learned end-to-end with off-the-shelf object detectors. This 'dynamic proposal processing' (DPP) is shown to outperform state-of-the-art end-to-end object detectors (DETR, Sparse R-CNN) by a clear margin for a given computational complexity.

cs.CV↗
arXiv2022

Improving Vision Transformers for Incremental Learning

This paper proposes a working recipe of using Vision Transformer (ViT) in class incremental learning. Although this recipe only combines existing techniques, developing the combination is not trivial. Firstly, naive application of ViT to replace convolutional neural networks (CNNs) in incremental learning results in serious performance degradation. Secondly, we nail down three issues of naively using ViT: (a) ViT has very slow convergence when the number of classes is small, (b) more bias towards new classes is observed in ViT than CNN-based architectures, and (c) the conventional learning rate of ViT is too low to learn a good classifier layer. Finally, our solution, named ViTIL (ViT for Incremental Learning) achieves new state-of-the-art on both CIFAR and ImageNet datasets for all three class incremental learning setups by a clear margin. We believe this advances the knowledge of transformer in the incremental learning community. Code will be publicly released.

cs.CV↗
arXiv2022

Terphenylthiazole-based self-assembled monolayers on cobalt with high conductance photo-switching ratio for spintronics

Two new photo-switchable terphenylthiazoles molecules are synthesized and self-assembled as monolayers on Au and on ferromagnetic Co electrodes. The electron transport properties probed by conductive atomic force microscopy in ultra-high vacuum reveal a conductance of the light-induced closed (c) form larger than for the open (o) form. We report an unprecedented conductance ratio up to 380 between the closed and open forms on Co for the molecule with the anchoring group (thiol) on the side of the two N atoms of the thiazole unit. This result is rationalized by Density Functional Theory (DFT) calculations coupled to the Non-Equilibrium Green's function (NEGF) formalism. These calculations show that the high conductance in the closed form is due to a strong electronic coupling between the terphenylthiazole molecules and the Co electrode that manifests by a resonant transmission peak at the Fermi energy of the Co electrode with a large broadening. This behavior is not observed for the same molecules self-assembled on gold electrodes. These high conductance ratios make these Co-based molecular junctions attractive candidates to develop and study switchable molecular spintronic devices.

cond-mat.mes-hall↗
arXiv2022

Self-aligned patterning technique for fabricating high-performance diamond sensor arrays with nanoscale precision

To efficiently align the creation of defect center with photonics structure in nanoscale precision is one of the outstanding challenges for realizing high-performance photonic devices and the application in quantum technology such as quantum sensing, scalable quantum systems, and quantum computing network. Here, we propose a facile self-aligned patterning technique wholly based on conventional engineering technology, with the doping precision can reach ~15nm. Specifically, we demonstrate this technique by fabricating diamond nanopillar sensor arrays, which show high consistency and near-optimal photon counts, high yield approaching the theoretical limit, and high filtering efficiency for different NV centers. Combined with appropriate crystal orientation, a saturated fluorescence rate of 4.65 Mcps and the best reported fluorescence-dependent detection sensitivity of 1900 cps^(-1/2) are achieved. This technique applicable to all similar solid-state systems should facilitate the development of parallel quantum sensing and scalable information processing.

physics.app-ph↗
arXiv2022

The Overlooked Classifier in Human-Object Interaction Recognition

Human-Object Interaction (HOI) recognition is challenging due to two factors: (1) significant imbalance across classes and (2) requiring multiple labels per image. This paper shows that these two challenges can be effectively addressed by improving the classifier with the backbone architecture untouched. Firstly, we encode the semantic correlation among classes into the classification head by initializing the weights with language embeddings of HOIs. As a result, the performance is boosted significantly, especially for the few-shot subset. Secondly, we propose a new loss named LSE-Sign to enhance multi-label learning on a long-tailed dataset. Our simple yet effective method enables detection-free HOI classification, outperforming the state-of-the-arts that require object detection and human pose by a clear margin. Moreover, we transfer the classification model to instance-level HOI detection by connecting it with an off-the-shelf object detector. We achieve state-of-the-art without additional fine-tuning.

cs.CV↗
arXiv2022

The Overlooked Classifier in Human-Object Interaction Recognition

Human-Object Interaction (HOI) recognition is challenging due to two factors: (1) significant imbalance across classes and (2) requiring multiple labels per image. This paper shows that these two challenges can be effectively addressed by improving the classifier with the backbone architecture untouched. Firstly, we encode the semantic correlation among classes into the classification head by initializing the weights with language embeddings of HOIs. As a result, the performance is boosted significantly, especially for the few-shot subset. Secondly, we propose a new loss named LSE-Sign to enhance multi-label learning on a long-tailed dataset. Our simple yet effective method enables detection-free HOI classification, outperforming the state-of-the-arts that require object detection and human pose by a clear margin. Moreover, we transfer the classification model to instance-level HOI detection by connecting it with an off-the-shelf object detector. We achieve state-of-the-art without additional fine-tuning.

cs.CV↗
arXiv2022

SA-VQA: Structured Alignment of Visual and Semantic Representations for Visual Question Answering

Visual Question Answering (VQA) attracts much attention from both industry and academia. As a multi-modality task, it is challenging since it requires not only visual and textual understanding, but also the ability to align cross-modality representations. Previous approaches extensively employ entity-level alignments, such as the correlations between the visual regions and their semantic labels, or the interactions across question words and object features. These attempts aim to improve the cross-modality representations, while ignoring their internal relations. Instead, we propose to apply structured alignments, which work with graph representation of visual and textual content, aiming to capture the deep connections between the visual and textual modalities. Nevertheless, it is nontrivial to represent and integrate graphs for structured alignments. In this work, we attempt to solve this issue by first converting different modality entities into sequential nodes and the adjacency graph, then incorporating them for structured alignments. As demonstrated in our experimental results, such a structured alignment improves reasoning performance. In addition, our model also exhibits better interpretability for each generated answer. The proposed model, without any pretraining, outperforms the state-of-the-art methods on GQA dataset, and beats the non-pretrained state-of-the-art methods on VQA-v2 dataset.

cs.CV↗
arXiv2022

Experimental Constraints on Exotic Spin-Dependent Interactions by a Magnetometer with Ensembles of Nitrogen-Vacancy Centers in Diamond

Improved constraints on exotic spin-dependent interactions are established at the micrometer scale by a magnetometer with ensembles of nitrogen-vacancy (NV) centers in diamond. A thin layer of NV electronic spin ensembles is utilized as the sensor, and a lead sphere is taken as the source of the nucleons. The exotic spin-dependent interactions are explored by detecting the possible effective magnetic fields by the sensor. Stringent bounds on an exotic parity-odd spin- and velocity-dependent interaction are set within the force range from 5 to 500 $μ$m. The upper limit of the corresponding coupling constant, $g_A^eg_V^N$, is improved by more than three orders of magnitude at 330 $μ$m. Improved constraints of $P, T$-violating scalar-pseudoscalar nucleon-electron interactions, are established within the force range from 6 to 45 $μ$m. The limit of the corresponding coupling constant, $g_S^Ng_P^e$, is improved by more than one order of magnitude at 30 $μ$m. Our result shows that a magnetometer with a NV ensemble can be a powerful platform for probing exotic spin-dependent interactions.

quant-ph↗
arXiv2021

Excited-state spectroscopy of spin defects in hexagonal boron nitride

We used optically detected magnetic resonance (ODMR) technique to directly probe electron-spin resonance transitions in the excited state of negatively-charged boron vacancy (VB-) defects in hexagonal boron nitride (hBN) at room temperature. The data showed that the excited state has a zero-field splitting of ~ 2.1 GHz, a g factor similar to the ground state and two types of hyperfine splitting ~ 90 MHz and ~ 18.8 MHz respectively. Pulsed ODMR experiments were conducted to further verify observed resonant peaks corresponding to spin transitions in the excited state. In addition, negative peaks in photoluminescence and ODMR contrast as a function of magnetic field magnitude and angle at level anti-crossing were observed and explained by coherent spin precession and anisotropic relaxation. This work provided significant insights for studying the structure of VB- excited states, which might be used for quantum information processing and nanoscale quantum sensing.

quant-ph↗
arXiv2021

A Unified Transferable Model for ML-Enhanced DBMS

Recently, the database management system (DBMS) community has witnessed the power of machine learning (ML) solutions for DBMS tasks. Despite their promising performance, these existing solutions can hardly be considered satisfactory. First, these ML-based methods in DBMS are not effective enough because they are optimized on each specific task, and cannot explore or understand the intrinsic connections between tasks. Second, the training process has serious limitations that hinder their practicality, because they need to retrain the entire model from scratch for a new DB. Moreover, for each retraining, they require an excessive amount of training data, which is very expensive to acquire and unavailable for a new DB. We propose to explore the transferabilities of the ML methods both across tasks and across DBs to tackle these fundamental drawbacks. In this paper, we propose a unified model MTMLF that uses a multi-task training procedure to capture the transferable knowledge across tasks and a pre-train fine-tune procedure to distill the transferable meta knowledge across DBs. We believe this paradigm is more suitable for cloud DB service, and has the potential to revolutionize the way how ML is used in DBMS. Furthermore, to demonstrate the predicting power and viability of MTMLF, we provide a concrete and very promising case study on query optimization tasks. Last but not least, we discuss several concrete research opportunities along this line of work.

cs.DB↗