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Xinyu Zhou

Publications and source records attributed to Xinyu Zhou.

At least 127 records · Page 7Linked to original sources
arXiv2023

Depth-Based Statistical Inferences in the Spike Train Space

Metric-based summary statistics such as mean and covariance have been introduced in neural spike train space. They can properly describe template and variability in spike train data, but are often sensitive to outliers and expensive to compute. Recent studies also examine outlier detection and classification methods on point processes. These tools provide reasonable and efficient result, whereas the accuracy remains at a low level in certain cases. In this study, we propose to adopt a well-established notion of statistical depth to the spike train space. This framework can naturally define the median in a set of spike trains, which provides a robust description of the 'center' or 'template' of the observations. It also provides a principled method to identify 'outliers' in the data and classify data from different categories. We systematically compare the median with the state-of-the-art 'mean spike trains' in terms of robustness and efficiency. The performance of our novel outlier detection and classification tools will be compared with previous methods. The result shows the median has superior description for 'template' than the mean. Moreover, the proposed outlier detection and classification perform more accurately than previous methods. The advantages and superiority are well illustrated with simulations and real data.

stat.AP↗
arXiv2023

Mobile Augmented Reality with Federated Learning in the Metaverse

The Metaverse is deemed the next evolution of the Internet and has received much attention recently. Metaverse applications via mobile augmented reality (MAR) require rapid and accurate object detection to mix digital data with the real world. As mobile devices evolve, their computational capabilities are increasing, and thus their computational resources can be leveraged to train machine learning models. In light of the increasing concerns of user privacy and data security, federated learning (FL) has become a promising distributed learning framework for privacy-preserving analytics. In this article, FL and MAR are brought together in the Metaverse. We discuss the necessity and rationality of the combination of FL and MAR. The prospective technologies that support FL and MAR in the Metaverse are also discussed. In addition, existing challenges that prevent the fulfillment of FL and MAR in the Metaverse and several application scenarios are presented. Finally, three case studies of Metaverse FL-MAR systems are demonstrated.

cs.LG↗
arXiv2023

A new connectivity bound for a tournament to be highly linked

A digraph $D$ is $k$-linked if for any pair of two disjoint sets $\{x_{1},x_{2},\ldots,x_{k}\}$ and $\{y_{1},y_{2},\ldots,y_{k}\}$ of vertices in $D$, there exist vertex disjoint dipaths $P_{1},P_{2},\ldots,P_{k}$ such that $P_{i}$ is a dipath from $x_{i}$ to $y_{i}$ for each $i\in[k]$. Pokrovskiy (JCTB, 2015) confirmed a conjecture of Kühn et al. (Proc. Lond. Math. Soc., 2014) by verifying that every $452k$-connected tournament is $k$-linked. Meng et al. (Eur. J. Comb., 2021) improved this upper bound by showing that any $(40k-31)$-connected tournament is $k$-linked. In this paper, we show a better upper bound by proving that every $\lceil 12.5k-6\rceil$-connected tournament with minimum out-degree at least $21k-14$ is $k$-linked. Furthermore, we improve a key lemma that was first introduced by Pokrovskiy (JCTB, 2015) and later enhanced by Meng et al. (Eur. J. Comb., 2021).

math.CO↗
arXiv2023

Revealing the frequency-dependent oscillations in the nonlinear terahertz response induced by the Josephson current

Nonlinear responses of superconductors to intense terahertz radiation has been an active research frontier. Using terahertz pump-terahertz probe spectroscopy, we investigate the c-axis nonlinear optical response of a high-temperature superconducting cuprate. After excitation by a single-cycle terahertz pump pulse, the reflectivity of the probe pulse oscillates as the pump-probe delay is varied. Interestingly, the oscillatory central frequency scales linearly with the probe frequency, a fact widely overlooked in pump-probe experiments. By theoretically solving the nonlinear optical reflection problem on the interface, we show that our observation is well explained by the Josephson-type third-order nonlinear electrodynamics, together with the emission coefficient from inside the material into free space. The latter results in a strong enhancement of the emitted signal whose physical frequency is around the Josephson plasma edge. Our result offers a benchmark for and new insights into strong-field terahertz spectroscopy of related quantum materials.

cond-mat.supr-con↗
arXiv2023

Logical Bias Learning for Object Relation Prediction

Scene graph generation (SGG) aims to automatically map an image into a semantic structural graph for better scene understanding. It has attracted significant attention for its ability to provide object and relation information, enabling graph reasoning for downstream tasks. However, it faces severe limitations in practice due to the biased data and training method. In this paper, we present a more rational and effective strategy based on causal inference for object relation prediction. To further evaluate the superiority of our strategy, we propose an object enhancement module to conduct ablation studies. Experimental results on the Visual Gnome 150 (VG-150) dataset demonstrate the effectiveness of our proposed method. These contributions can provide great potential for foundation models for decision-making.

cs.CV↗
arXiv2023

Towards Joint Modeling of Dialogue Response and Speech Synthesis based on Large Language Model

This paper explores the potential of constructing an AI spoken dialogue system that "thinks how to respond" and "thinks how to speak" simultaneously, which more closely aligns with the human speech production process compared to the current cascade pipeline of independent chatbot and Text-to-Speech (TTS) modules. We hypothesize that Large Language Models (LLMs) with billions of parameters possess significant speech understanding capabilities and can jointly model dialogue responses and linguistic features. We conduct two sets of experiments: 1) Prosodic structure prediction, a typical front-end task in TTS, demonstrating the speech understanding ability of LLMs, and 2) Further integrating dialogue response and a wide array of linguistic features using a unified encoding format. Our results indicate that the LLM-based approach is a promising direction for building unified spoken dialogue systems.

cs.CL↗
arXiv2023

Few shot font generation via transferring similarity guided global style and quantization local style

Automatic few-shot font generation (AFFG), aiming at generating new fonts with only a few glyph references, reduces the labor cost of manually designing fonts. However, the traditional AFFG paradigm of style-content disentanglement cannot capture the diverse local details of different fonts. So, many component-based approaches are proposed to tackle this problem. The issue with component-based approaches is that they usually require special pre-defined glyph components, e.g., strokes and radicals, which is infeasible for AFFG of different languages. In this paper, we present a novel font generation approach by aggregating styles from character similarity-guided global features and stylized component-level representations. We calculate the similarity scores of the target character and the referenced samples by measuring the distance along the corresponding channels from the content features, and assigning them as the weights for aggregating the global style features. To better capture the local styles, a cross-attention-based style transfer module is adopted to transfer the styles of reference glyphs to the components, where the components are self-learned discrete latent codes through vector quantization without manual definition. With these designs, our AFFG method could obtain a complete set of component-level style representations, and also control the global glyph characteristics. The experimental results reflect the effectiveness and generalization of the proposed method on different linguistic scripts, and also show its superiority when compared with other state-of-the-art methods. The source code can be found at https://github.com/awei669/VQ-Font.

cs.CV↗
arXiv2023

Direct visualization of electric current induced dipoles of atomic impurities

Learning the electron scattering around atomic impurities is a fundamental step to fully understand the basic electronic transport properties of realistic conducting materials. Although many efforts have been made in this field for several decades, atomic scale transport around single point-like impurities has yet been achieved. Here, we report the direct visualization of the electric current induced dipoles around single atomic impurities in epitaxial bilayer graphene by multi-probe low temperature scanning tunneling potentiometry as the local current density is raised up to around 25 A/m, which is considerably higher than that in previous studies. We find the directions of these dipoles which are parallel or anti-parallel to local current are determined by the charge polarity of the impurities, revealing the direct evidence for the existence of the carrier density modulation effect proposed by Landauer in 1976. Furthermore, by $in$ $situ$ tuning local current direction with contact probes, these dipoles are redirected correspondingly. Our work paves the way to explore the electronic quantum transport phenomena at single atomic impurity level and the potential future electronics toward or beyond the end of Moore's Law.

cond-mat.mes-hall↗
arXiv2023

NBMOD: Find It and Grasp It in Noisy Background

Grasping objects is a fundamental yet important capability of robots, and many tasks such as sorting and picking rely on this skill. The prerequisite for stable grasping is the ability to correctly identify suitable grasping positions. However, finding appropriate grasping points is challenging due to the diverse shapes, varying density distributions, and significant differences between the barycenter of various objects. In the past few years, researchers have proposed many methods to address the above-mentioned issues and achieved very good results on publicly available datasets such as the Cornell dataset and the Jacquard dataset. The problem is that the backgrounds of Cornell and Jacquard datasets are relatively simple - typically just a whiteboard, while in real-world operational environments, the background could be complex and noisy. Moreover, in real-world scenarios, robots usually only need to grasp fixed types of objects. To address the aforementioned issues, we proposed a large-scale grasp detection dataset called NBMOD: Noisy Background Multi-Object Dataset for grasp detection, which consists of 31,500 RGB-D images of 20 different types of fruits. Accurate prediction of angles has always been a challenging problem in the detection task of oriented bounding boxes. This paper presents a Rotation Anchor Mechanism (RAM) to address this issue. Considering the high real-time requirement of robotic systems, we propose a series of lightweight architectures called RA-GraspNet (GraspNet with Rotation Anchor): RARA (network with Rotation Anchor and Region Attention), RAST (network with Rotation Anchor and Semi Transformer), and RAGT (network with Rotation Anchor and Global Transformer) to tackle this problem. Among them, the RAGT-3/3 model achieves an accuracy of 99% on the NBMOD dataset. The NBMOD and our code are available at https://github.com/kmittle/Grasp-Detection-NBMOD.

cs.CV↗
arXiv2023

Optimizing Utility-Energy Efficiency for the Metaverse over Wireless Networks under Physical Layer Security

The Metaverse, an emerging digital space, is expected to offer various services mirroring the real world. Wireless communications for mobile Metaverse users should be tailored to meet the following user characteristics: 1) emphasizing application-specific perceptual utility instead of simply the transmission rate, 2) concerned with energy efficiency due to the limited device battery and energy intensiveness of some applications, and 3) caring about security as the applications may involve sensitive personal data. To this end, this paper incorporates application-specific utility, energy efficiency, and physical-layer security (PLS) into the studied optimization in a wireless network for the Metaverse. Specifically, after introducing utility-energy efficiency (UEE) to represent each Metaverse user's application-specific objective under PLS, we formulate an optimization to maximize the network's weighted sum-UEE by deciding users' transmission powers and communication bandwidths. The formulated problem belongs to the sum-of-ratios optimization, for which prior studies have demonstrated its difficulty. Nevertheless, our proposed algorithm 1) obtains the global optimum for the weighted sum-UEE optimization, via a transform to parametric convex optimization problems, 2) applies to any utility function which is concave, increasing, and twice differentiable, and 3) achieves a linear time complexity in the number of users (the optimal complexity in the order sense). Simulations confirm the superiority of our algorithm over other approaches. We explain that our technique for solving the sum-of-ratios optimization is applicable to other optimization problems in wireless networks and mobile computing.

cs.SI↗
arXiv2023

Resource Allocation of Federated Learning Assisted Mobile Augmented Reality System in the Metaverse

Metaverse has become a buzzword recently. Mobile augmented reality (MAR) is a promising approach to providing users with an immersive experience in the Metaverse. However, due to limitations of bandwidth, latency and computational resources, MAR cannot be applied on a large scale in the Metaverse yet. Moreover, federated learning, with its privacy-preserving characteristics, has emerged as a prospective distributed learning framework in the future Metaverse world. In this paper, we propose a federated learning assisted MAR system via non-orthogonal multiple access for the Metaverse. Additionally, to optimize a weighted sum of energy, latency and model accuracy, a resource allocation algorithm is devised by setting appropriate transmission power, CPU frequency and video frame resolution for each user. Experimental results demonstrate that our proposed algorithm achieves an overall good performance compared to a random algorithm and greedy algorithm.

cs.SI↗
arXiv2023

Optical spectroscopy and band structure calculations of structural phase transition in the Vanadium-based kagome metal ScV$_6$Sn$_6$

In condensed matter physics, materials with kagome lattice display a range of exotic quantum states, including charge density wave (CDW), superconductivity and magnetism. Recently, the intermetallic kagome metal ScV6Sn6 was discovered to undergo a first-order structural phase transition with the formation of a root3xroot3x3 CDW at around 92 K. The bulk electronic band properties are crucial to understanding the origin of the structural phase transition. Here, we conducted an optical spectroscopy study in combination with band structure calculations across the structural transition. Our findings showed abrupt changes in the optical reflectivity/conductivity spectra as a result of the structural transition, without any observable gap formation behavior. The optical measurements and band calculations actually reveal a sudden change of the band structure after transition. It is important to note that this phase transition is of the first-order type, which distinguishes it from conventional density-wave type condensations. Our results provide an insight into the origin of the structural phase transition in this new and unique kagome lattice.

cond-mat.str-el↗
arXiv2022

Strong nonlinear optical response and transient symmetry switching in Type-II Weyl semimetal $β$-WP2

The topological Weyl semimetals with peculiar band structure exhibit novel nonlinear optical enhancement phenomena even for light at optical wavelengths. While many intriguing nonlinear optical effects were constantly uncovered in type-I semimetals, few experimental works focused on basic nonlinear optical properties in type-II Weyl semimetals. Here we perform a fundamental static and time-resolved second harmonic generation (SHG) on the three dimensional Type-II Weyl semimetal candidate $β$-WP$_2$. Although $β$-WP$_2$ exhibits extremely high conductivity and an extraordinarily large mean free path, the second harmonic generation is unscreened by conduction electrons, we observed rather strong SHG response compared to non-topological polar metals and archetypal ferroelectric insulators. Additionally, our time-resolved SHG experiment traces ultrafast symmetry switching and reveals that polar metal $β$-WP$_2$ tends to form inversion symmetric metastable state after photo-excitation. Intense femtosecond laser pulse could optically drive symmetry switching and tune nonlinear optical response on ultrafast timescales although the interlayer coupling of $β$-WP$_2$ is very strong. Our work is illuminating for the polar metal nonlinear optics and potential ultrafast topological optoelectronic applications.

cond-mat.mtrl-sci↗
arXiv2022

Nanorobot queue: Cooperative treatment of cancer based on team member communication and image processing

Although nanorobots have been used as clinical prescriptions for work such as gastroscopy, and even photoacoustic tomography technology has been proposed to control nanorobots to deliver drugs at designated delivery points in real time, and there are cases of eliminating "superbacteria" in blood through nanorobots, most technologies are immature, either with low efficiency or low accuracy, Either it can not be mass produced, so the most effective way to treat cancer diseases at this stage is through chemotherapy and radiotherapy. Patients are suffering and can not be cured. Therefore, this paper proposes an ideal model of a treatment method that can completely cure cancer, a cooperative treatment method based on nano robot queue through team member communication and computer vision image classification (target detection).

cs.RO↗
arXiv2022

A Stochastic Process Model for Time Warping Functions

Time warping function provides a mathematical representation to measure phase variability in functional data. Recent studies have developed various approaches to estimate optimal warping between functions and provide non-Euclidean models. However, a principled, linear, generative model on time warping functions is still under-explored. This is a highly challenging problem because the space of warping functions is non-linear with the conventional Euclidean metric. To address this problem, we propose a stochastic process model for time warping functions, where the key is to define a linear, inner-product structure on the time warping space and then transform the warping functions into a sub-space of the $\mathbb L^2$ Euclidean space. With certain constraints on the warping functions, this transformation is an isometric isomorphism. In the transformed space, we adopt the $\mathbb L^2$ basis in the Hilbert space for representation. This new framework can easily build generative model on time warping by using different types of stochastic process. It can also be used to conduct statistical inferences such as functional PCA, functional ANOVA, and functional regressions. Furthermore, we demonstrate the effectiveness of this new framework by using it as a new prior in the Bayesian registration, and propose an efficient gradient method to address the important maximum a posteriori estimation. We illustrate the new Bayesian method using simulations which properly characterize nonuniform and correlated constraints in the time domain. Finally, we apply the new framework to the famous Berkeley growth data and obtain reasonable results on modeling, resampling, group comparison, and classification analysis.

stat.ME↗
arXiv2022

A New Learning Paradigm for Stochastic Configuration Network: SCN+

Learning using privileged information (LUPI) paradigm, which pioneered teacher-student interaction mechanism, makes the learning models use additional information in training stage. This paper is the first to propose an incremental learning algorithm with LUPI paradigm for stochastic configuration network (SCN), named SCN+. This novel algorithm can leverage privileged information into SCN in the training stage, which provides a new method to train SCN. Moreover, the convergences have been studied in this paper. Finally, experimental results indicate that SCN+ indeed performs favorably.

cs.LG↗
arXiv2022

Statistical Depth for Point Process via the Isometric Log-Ratio Transformation

Statistical depth, a useful tool to measure the center-outward rank of multivariate and functional data, is still under-explored in temporal point processes. Recent studies on point process depth proposed a weighted product of two terms - one indicates the depth of the cardinality of the process, and the other characterizes the conditional depth of the temporal events given the cardinality. The second term is of great challenge because of the apparent nonlinear structure of event times, and so far only basic parametric representations such as Gaussian and Dirichlet densities were adopted in the definitions. However, these simplified forms ignore the underlying distribution of the process events, which makes the methods difficult to interpret and to apply to complicated patterns. To deal with these problems, we in this paper propose a distribution-based approach to the conditional depth via the well-known Isometric Log-Ratio (ILR) transformation on the inter-event times. The new depth, called the ILR depth, is at first defined for homogeneous Poisson process by using the density function on the transformed space. The definition is then extended to any general point process via a time-rescaling transformation. We illustrate the ILR depth using simulations of Poisson and non-Poisson processes and demonstrate its superiority over previous methods. We also thoroughly examine its mathematical properties and asymptotics in large samples. Finally, we apply the ILR depth in a real dataset and the result clearly shows the effectiveness of the new method.

stat.ME↗
arXiv2021

A Review of Automated Diagnosis of COVID-19 Based on Scanning Images

The pandemic of COVID-19 has caused millions of infections, which has led to a great loss all over the world, socially and economically. Due to the false-negative rate and the time-consuming of the conventional Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, diagnosing based on X-ray images and Computed Tomography (CT) images has been widely adopted. Therefore, researchers of the computer vision area have developed many automatic diagnosing models based on machine learning or deep learning to assist the radiologists and improve the diagnosing accuracy. In this paper, we present a review of these recently emerging automatic diagnosing models. 70 models proposed from February 14, 2020, to July 21, 2020, are involved. We analyzed the models from the perspective of preprocessing, feature extraction, classification, and evaluation. Based on the limitation of existing models, we pointed out that domain adaption in transfer learning and interpretability promotion would be the possible future directions.

eess.IV↗