arXiv subjects

Xiaoyu Guo

Publications and source records attributed to Xiaoyu Guo.

At least 55 records · Page 3Linked to original sources

arXiv2024

Tracking Reflected Objects: A Benchmark

Visual tracking has advanced significantly in recent years, mainly due to the availability of large-scale training datasets. These datasets have enabled the development of numerous algorithms that can track objects with high accuracy and robustness.However, the majority of current research has been directed towards tracking generic objects, with less emphasis on more specialized and challenging scenarios. One such challenging scenario involves tracking reflected objects. Reflections can significantly distort the appearance of objects, creating ambiguous visual cues that complicate the tracking process. This issue is particularly pertinent in applications such as autonomous driving, security, smart homes, and industrial production, where accurately tracking objects reflected in surfaces like mirrors or glass is crucial. To address this gap, we introduce TRO, a benchmark specifically for Tracking Reflected Objects. TRO includes 200 sequences with around 70,000 frames, each carefully annotated with bounding boxes. This dataset aims to encourage the development of new, accurate methods for tracking reflected objects, which present unique challenges not sufficiently covered by existing benchmarks. We evaluated 20 state-of-the-art trackers and found that they struggle with the complexities of reflections. To provide a stronger baseline, we propose a new tracker, HiP-HaTrack, which uses hierarchical features to improve performance, significantly outperforming existing algorithms. We believe our benchmark, evaluation, and HiP-HaTrack will inspire further research and applications in tracking reflected objects. The TRO and code are available at https://github.com/OpenCodeGithub/HIP-HaTrack.

cs.CV↗

arXiv2024

Quantum Circuit Ansatz: Patterns of Abstraction and Reuse of Quantum Algorithm Design

Quantum computing holds the potential to revolutionize various fields by efficiently tackling complex problems. At its core are quantum circuits, sequences of quantum gates manipulating quantum states. The selection of the right quantum circuit ansatz, which defines initial circuit structures and serves as the basis for optimization techniques, is crucial in quantum algorithm design.This paper presents a categorized catalog of quantum circuit ansatzes aimed at supporting quantum algorithm design and implementation. Each ansatz is described with details such as intent, motivation, applicability, circuit diagram, implementation, example, and see also. Practical examples are provided to illustrate their application in quantum algorithm design.The catalog aims to assist quantum algorithm designers by offering insights into the strengths and limitations of different ansatzes, thereby facilitating decision-making for specific tasks.

cs.SE↗

arXiv2024

On Repairing Quantum Programs Using ChatGPT

Automated Program Repair (APR) is a vital area in software engineering aimed at generating automatic patches for vulnerable programs. While numerous techniques have been proposed for repairing classical programs, the realm of quantum programming lacks a comparable automated repair technique. In this initial exploration, we investigate the use of ChatGPT for quantum program repair and evaluate its performance on Bugs4Q, a benchmark suite of quantum program bugs. Our findings demonstrate the feasibility of employing ChatGPT for quantum program repair. Specifically, we assess ChatGPT's ability to address bugs within the Bugs4Q benchmark, revealing its success in repairing 29 out of 38 bugs. This research represents a promising step towards automating the repair process for quantum programs.

cs.SE↗

arXiv2024

Progress and Prospects in Two-Dimensional Magnetism of van der Waals Materials

Two-dimensional (2D) magnetism in van der Waals (vdW) atomic crystals and moiré superlattices has emerged as a topic of tremendous interest in the fields of condensed matter physics and materials science within the past half-decade since its first experimental discovery in 2016 - 2017. It has not only served as a powerful platform for investigating phase transitions in the 2D limit and exploring new phases of matter, but also provided new opportunities for applications in microelectronics, spintronics, magnonics, optomagnetics, and so on. Despite the flourishing developments in 2D magnetism over this short period of time, further efforts are welcome in multiple forefronts of 2D magnetism research for achieving the ultimate goal of routinely implementing 2D magnets as quantum electronic components. In this review article, we will start with basic concepts and properties of 2D magnetism, followed by a brief overview of historical efforts in 2D magnetism research and then a comprehensive review of vdW material-based 2D magnetism. We will conclude with discussions on potential future research directions for this growing field of 2D vdW magnetism.

cond-mat.mtrl-sci↗

arXiv2023

Electric quadrupole second harmonic generation revealing dual magnetic orders in a magnetic Weyl semimetal

Broken symmetries and electronic topology are nicely manifested together in the second order nonlinear optical responses from topologically nontrivial materials. While second order nonlinear optical effects from the electric dipole (ED) contribution have been extensively explored in polar Weyl semimetals (WSMs) with broken spatial inversion (SI) symmetry, they are rarely studied in centrosymmetric magnetic WSMs with broken time reversal (TR) symmetry due to complete suppression of the ED contribution. Here, we report experimental demonstration of optical second harmonic generation (SHG) in a magnetic WSM Co$_{3}$Sn$_{2}$S$_{2}$ from the electric quadrupole (EQ) contribution. By tracking the temperature dependence of the rotation anisotropy (RA) of SHG, we capture two magnetic phase transitions, with both the SHG intensity increasing and its RA pattern rotating at $T_{C,1}$=175K and $T_{C,2}$=120K subsequently. The fitted critical exponents for the SHG intensity and RA orientation near $T_{C,1}$ and $T_{C,2}$ suggest that the magnetic phase at $T_{C,1}$ is a 3D Ising-type out-of-plane ferromagnetism while the other at $T_{C,2}$ is a 3D XY-type all-in-all-out in-plane antiferromagnetism. Our results show the success of detection and exploration of EQ SHG in a centrosymmetric magnetic WSM, and hence open the pathway towards the future investigation of its tie to the band topology.

cond-mat.mtrl-sci↗

arXiv2023

"Extraordinary" Phase Transition Revealed in a van der Waals Antiferromagnet

While the surface-bulk correspondence has been ubiquitously shown in topological phases, the relationship between surface and bulk in Landau-like phases is much less explored. Theoretical investigations since 1970s for semi-infinite systems have predicted the possibility of the surface order emerging at a higher temperature than the bulk, clearly illustrating a counterintuitive situation and greatly enriching phase transitions. But experimental realizations of this prediction remain missing. Here, we demonstrate the higher-temperature surface and lower-temperature bulk phase transitions in CrSBr, a van der Waals (vdW) layered antiferromagnet. We leverage the surface sensitivity of electric dipole second harmonic generation (SHG) to resolve surface magnetism, the bulk nature of electric quadrupole SHG to probe bulk spin correlations, and their interference to capture the two magnetic domain states. Our density functional theory calculations show the suppression of ferromagnetic-antiferromagnetic competition at the surface responsible for this enhanced surface magnetism. Our results not only show unexpected, richer phase transitions in vdW magnets, but also provide viable ways to enhance magnetism in their 2D form.

cond-mat.mtrl-sci↗

arXiv2023

Deep Learning Applications Based on WISE Infrared Data: Classification of Stars, Galaxies and Quasars

The Wide-field Infrared Survey Explorer (WISE) has detected hundreds of millions of sources over the entire sky. However, classifying them reliably is a great challenge due to degeneracies in WISE multicolor space and low detection levels in its two longest-wavelength bandpasses. In this paper, the deep learning classification network, IICnet (Infrared Image Classification network), is designed to classify sources from WISE images to achieve a more accurate classification goal. IICnet shows good ability on the feature extraction of the WISE sources. Experiments demonstrates that the classification results of IICnet are superior to some other methods; it has obtained 96.2% accuracy for galaxies, 97.9% accuracy for quasars, and 96.4% accuracy for stars, and the Area Under Curve (AUC) of the IICnet classifier can reach more than 99%. In addition, the superiority of IICnet in processing infrared images has been demonstrated in the comparisons with VGG16, GoogleNet, ResNet34, MobileNet, EfficientNetV2, and RepVGG-fewer parameters and faster inference. The above proves that IICnet is an effective method to classify infrared sources.

astro-ph.IM↗

arXiv2023

Prompt What You Need: Enhancing Segmentation in Rainy Scenes with Anchor-based Prompting

Semantic segmentation in rainy scenes is a challenging task due to the complex environment, class distribution imbalance, and limited annotated data. To address these challenges, we propose a novel framework that utilizes semi-supervised learning and pre-trained segmentation foundation model to achieve superior performance. Specifically, our framework leverages the semi-supervised model as the basis for generating raw semantic segmentation results, while also serving as a guiding force to prompt pre-trained foundation model to compensate for knowledge gaps with entropy-based anchors. In addition, to minimize the impact of irrelevant segmentation masks generated by the pre-trained foundation model, we also propose a mask filtering and fusion mechanism that optimizes raw semantic segmentation results based on the principle of minimum risk. The proposed framework achieves superior segmentation performance on the Rainy WCity dataset and is awarded the first prize in the sub-track of STRAIN in ICME 2023 Grand Challenges.

cs.CV↗

arXiv2023

NUAA-QMUL-AIIT at Memotion 3: Multi-modal Fusion with Squeeze-and-Excitation for Internet Meme Emotion Analysis

This paper describes the participation of our NUAA-QMUL-AIIT team in the Memotion 3 shared task on meme emotion analysis. We propose a novel multi-modal fusion method, Squeeze-and-Excitation Fusion (SEFusion), and embed it into our system for emotion classification in memes. SEFusion is a simple fusion method that employs fully connected layers, reshaping, and matrix multiplication. SEFusion learns a weight for each modality and then applies it to its own modality feature. We evaluate the performance of our system on the three Memotion 3 sub-tasks. Among all participating systems in this Memotion 3 shared task, our system ranked first on task A, fifth on task B, and second on task C. Our proposed SEFusion provides the flexibility to fuse any features from different modalities. The source code for our method is published on https://github.com/xxxxxxxxy/memotion3-SEFusion.

cs.CL↗

arXiv2023

Cluster-based Deep Ensemble Learning for Emotion Classification in Internet Memes

Memes have gained popularity as a means to share visual ideas through the Internet and social media by mixing text, images and videos, often for humorous purposes. Research enabling automated analysis of memes has gained attention in recent years, including among others the task of classifying the emotion expressed in memes. In this paper, we propose a novel model, cluster-based deep ensemble learning (CDEL), for emotion classification in memes. CDEL is a hybrid model that leverages the benefits of a deep learning model in combination with a clustering algorithm, which enhances the model with additional information after clustering memes with similar facial features. We evaluate the performance of CDEL on a benchmark dataset for emotion classification, proving its effectiveness by outperforming a wide range of baseline models and achieving state-of-the-art performance. Further evaluation through ablated models demonstrates the effectiveness of the different components of CDEL.

cs.CV↗

arXiv2023

Data-Driven Load-Current Sharing Control for Multi-Stack Fuel Cell System with Circulating Current Mitigation

The global trend toward renewable power generation has drawn great attention to hydrogen Fuel Cells (FCs), which have a wide variety of applications, from utility power stations to laptops. The Multi-stack Fuel Cell System (MFCS), which is an assembly of FC stacks, can be a remedy for obstacles in high-power applications. However, the output voltage of FC stacks varies dramatically under variable load conditions; hence, in order for MFCS to be efficiently operated and guarantee an appropriate load-current sharing among the FC stacks, advanced converter controllers for power conditioning need to be designed. An accurate circuit model is essential for controller design, which accounts for the fact that the parameters of some converter components may change due to aging and repetitive stress in long-term operations. Existing control frameworks and parametric and non-parametric system identification techniques do not consider the aforementioned challenges. Thus, this paper investigates the potential of a data-driven method that, without system identification, directly implements control on paralleled converters using raw data. Based on pre-collected input/output trajectories, a non-parametric representation of the overall circuit is produced for implementing predictive control. While approaching equal current sharing within the MFCS, the proposed method considers the minimization of load-following error and mitigation of circulating current between the converters. Simulation results verify the effectiveness of the proposed method.

eess.SY↗

arXiv2022

Ferro-rotational domain walls revealed by electric quadrupole second harmonic generation microscopy

Domain walls are ubiquitous in materials that undergo phase transitions driven by spontaneous symmetry breaking. Domain walls in ferroics and multiferroics have received tremendous attention recently due to their emergent properties distinct from their domain counterparts, for example, their high mobility and controllability, as well as their potential applications in nanoelectronics. However, it is extremely challenging to detect, visualize and study the ferro-rotational (FR) domain walls because the FR order, in contrast to ferromagnetism (FM) and ferroelectricity (FE), is invariant under both the spatial-inversion and the time-reversal operations and thus hardly couple with conventional experimental probes. Here, an FR candidate $\mathrm{NiTiO_{3}}$ is investigated by ultrasensitive electric quadrupole (EQ) second harmonic generation rotational anisotropy (SHG RA) to probe the point symmetries of the two degenerate FR domain states, showing their relation by the vertical mirror operations that are broken below the FR critical temperature. We then visualize the real-space FR domains by scanning EQ SHG microscopy, and further resolve the FR domain walls by revealing a suppressed SHG intensity at domain walls. By taking local EQ SHG RA measurements, we show the restoration of the mirror symmetry at FR domain walls and prove their unconventional nonpolar nature. Our findings not only provide a comprehensive insight into FR domain walls, but also demonstrate a unique and powerful tool for future studies on domain walls of unconventional ferroics, both of which pave the way towards future manipulations and applications of FR domain walls.

cond-mat.mtrl-sci↗

arXiv2022

A three-stage magnetic phase transition revealed in ultrahigh-quality van der Waals magnet CrSBr

van der Waals (vdW) magnets are receiving ever-growing attention nowadays due to their significance in both fundamental research on low-dimensional magnetism and potential applications in spintronic devices. High crystalline quality of vdW magnets is key for maintaining intrinsic magnetic and electronic properties, especially when exfoliated down to the 2D limit. Here, ultrahigh-quality air-stable vdW CrSBr crystals are synthesized using the direct vapor-solid synthesis method. The high single crystallinity and spatial homogeneity have been thoroughly evidenced at length scales from sub-mm to atomic resolution by X-ray diffraction, second harmonic generation, and scanning transmission electron microscopy. More importantly, specific heat measurements of these ultrahigh quality CrSBr crystals show three thermodynamic anomalies at 185K, 156K, and 132K, revealing a stage-by-stage development of the magnetic order upon cooling, which is also corroborated with the magnetization and transport results. Our ultrahigh-quality CrSBr can further be exfoliated down to monolayers and bilayers easily, paving the way to integrate them into heterostructures for spintronic and magneto-optoelectronic applications.

cond-mat.mtrl-sci↗

arXiv2021

Structural Monoclinicity and Its Coupling to Layered Magnetism in Few-Layer $\mathrm{CrI_{3}}$

Using polarization-resolved Raman spectroscopy, we investigate layer number, temperature, and magnetic field dependence of Raman spectra in one- to four-layer $\mathrm{CrI_{3}}$. Layer-number-dependent Raman spectra show that in the paramagnetic phase a doubly degenerated $E_{g}$ mode of monolayer $\mathrm{CrI_{3}}$ splits into one $A_{g}$ and one $B_{g}$ mode in N-layer (N > 1) $\mathrm{CrI_{3}}$ due to the monoclinic stacking. Their energy separation increases in thicker samples until an eventual saturation. Temperature-dependent measurements further show that the split modes tend to merge upon cooling but remain separated until 10 K, indicating a failed attempt of the monoclinic-to-rhombohedral structural phase transition that is present in the bulk crystal. Magnetic-field-dependent measurements reveal an additional monoclinic distortion across the magnetic-field-induced layered antiferromagnetism-to-ferromagnetism phase transition. We propose a structural change that consists of both a lateral sliding toward the rhombohedral stacking and a decrease in the interlayer distance to explain our experimental observations.

cond-mat.mes-hall↗

arXiv2021

COVID-19 and Social Distancing: Disparities in Mobility Adaptation between Income Groups

In response to the coronavirus disease 2019 (COVID-19) pandemic, governments have encouraged and ordered citizens to practice social distancing, particularly by working and studying at home. Intuitively, only a subset of people have the ability to practice remote work. However, there has been little research on the disparity of mobility adaptation across different income groups in US cities during the pandemic. The authors worked to fill this gap by quantifying the impacts of the pandemic on human mobility by income in Greater Houston, Texas. In this paper, we determined human mobility using pseudonymized, spatially disaggregated cell phone location data. A longitudinal study across estimated income groups was conducted by measuring the total travel distance, radius of gyration, number of visited locations, and per-trip distance in April 2020 compared to the data in a baseline. An apparent disparity in mobility was found across estimated income groups. In particular, there was a strong negative correlation ($ρ$ = -0.90) between a traveler's estimated income and travel distance in April. Disparities in mobility adaptability were further shown since those in higher income brackets experienced larger percentage drops in the radius of gyration and the number of distinct visited locations than did those in lower income brackets. The findings of this study suggest a need to understand the reasons behind the mobility inflexibility among low-income populations during the pandemic. The study illuminates an equity issue which may be of interest to policy makers and researchers alike in the wake of an epidemic.

cs.CY↗

arXiv2020

NUAA-QMUL at SemEval-2020 Task 8: Utilizing BERT and DenseNet for Internet Meme Emotion Analysis

This paper describes our contribution to SemEval 2020 Task 8: Memotion Analysis. Our system learns multi-modal embeddings from text and images in order to classify Internet memes by sentiment. Our model learns text embeddings using BERT and extracts features from images with DenseNet, subsequently combining both features through concatenation. We also compare our results with those produced by DenseNet, ResNet, BERT, and BERT-ResNet. Our results show that image classification models have the potential to help classifying memes, with DenseNet outperforming ResNet. Adding text features is however not always helpful for Memotion Analysis.

cs.CL↗

arXiv2020

Compose Like Humans: Jointly Improving the Coherence and Novelty for Modern Chinese Poetry Generation

Chinese poetry is an important part of worldwide culture, and classical and modern sub-branches are quite different. The former is a unique genre and has strict constraints, while the latter is very flexible in length, optional to have rhymes, and similar to modern poetry in other languages. Thus, it requires more to control the coherence and improve the novelty. In this paper, we propose a generate-retrieve-then-refine paradigm to jointly improve the coherence and novelty. In the first stage, a draft is generated given keywords (i.e., topics) only. The second stage produces a "refining vector" from retrieval lines. At last, we take into consideration both the draft and the "refining vector" to generate a new poem. The draft provides future sentence-level information for a line to be generated. Meanwhile, the "refining vector" points out the direction of refinement based on impressive words detection mechanism which can learn good patterns from references and then create new ones via insertion operation. Experimental results on a collected large-scale modern Chinese poetry dataset show that our proposed approach can not only generate more coherent poems, but also improve the diversity and novelty.

cs.CL↗

arXiv2019

Angular dependence of the upper critical field in the high-pressure $1T'$ phase of MoTe$_2$

Superconductivity in the type-II Weyl semimetal candidate MoTe$_2$ has attracted much attention due to the possible realization of topological superconductivity. Under applied pressure, the superconducting transition temperature is significantly enhanced, while the structural transition from the high-temperature 1$T'$ phase to the low-temperature $T_d$ phase is suppressed. Hence, applying pressure allows us to investigate the dimensionality of superconductivity in 1$T'$-MoTe$_2$. We have performed a detailed study of the magnetotransport properties and upper critical field $H_{c2}$ of MoTe$_2$ under pressure. The magnetoresistance (MR) and Hall coefficient of MoTe$_2$ are found to be decreasing with increasing pressure. In addition, the Kohler's scalings for the MR data above $\sim$11 kbar show a change of exponent whereas the data at lower pressure can be well scaled with a single exponent. These results are suggestive of a Fermi surface reconstruction when the structure changes from the $T_d$ to 1$T'$ phase. The $H_{c2}$-temperature phase diagram constructed at 15 kbar, with $H\parallel ab$ and $H\perp ab$, can be satisfactorily described by the Werthamer-Helfand-Hohenberg model with the Maki parameters $α\sim$ 0.77 and 0.45, respectively. The relatively large $α$ may stem from a small Fermi surface and a large effective mass of semimetallic MoTe$_2$. The angular dependence of $H_{c2}$ at 15 kbar can be well fitted by the Tinkham model, suggesting the two-dimensional nature of superconductivity in the high-pressure 1$T'$ phase.

cond-mat.supr-con↗