arXiv subjects

Jing Zhou

Publications and source records attributed to Jing Zhou.

At least 55 records · Page 3Linked to original sources

arXiv2025

Holographic Schwinger effect in flavor-dependent systems

The holographic Schwinger effect is investigated in systems with $N_{f}=0$, $N_{f}=2$, and $N_{f}=2+1$ using the Einstein-Maxwell-dilaton (EMD) model, incorporating equation of state and baryon number susceptibility information from lattice quantum chromodynamics (QCD). It is found that the critical electric field is smallest for $N_{f}=0$, indicating that the Schwinger effect is more likely to occur than in systems with $N_{f}=2$ and $N_{f}=2+1$. The critical electric field decreases with increasing chemical potential and temperature across all systems. Additionally, potential analysis confirms that the maximum total potential energy increases with the number of flavors, suggesting that existing particles may reduce the probability of particle pair production.

hep-ph↗

arXiv2024

Cavity Plasmon: Enhanced Luminescence Effect on InGaN Light Emitting Diodes

We fabricated polygonal nanoholes in the top p-GaN layer of the InGaN/GaN light-emitting diode, followed by the deposition of Au/Al metal thin film within the nanoholes to create metal microcavities, thereby constructing the surface plasmon structure. The findings indicate that with increased current injection, the light output of the LEDs rose by 46%, accompanied by a shift of the gain peak position towards the plasmon resonance energy. The maximum enhancement factor increases to 2.38 as the coupling distance decreases from 60 nm to 30 nm. Interestingly, time-resolved photoluminescence data showed that the spontaneous emission decay time lengthened due to the plasmon coupling, suggesting the presence of a new plasmon coupling mechanism. Finite-Difference Time-Domain simulation results show that the electric field is localized at certain locations around the metal microcavity, generating a new type of shape-sensitive plasmon, named Cavity Plasmon here. This intense localization leads to a longer lifetime and enhances the recombination efficiency of excitons. We discuss several unique properties of the cavity plasmon generated by the polygonal metal microcavity with several specific angular shapes. The results demonstrate that the cavity plasmon generated by the polygonal metal microcavity is a highly promising technique for enhancing the light emission performance of of relevant semiconductor optoelectronic devices.

physics.optics↗

arXiv2024

Leveraging Black-box Models to Assess Feature Importance in Unconditional Distribution

Understanding how changes in explanatory features affect the unconditional distribution of the outcome is important in many applications. However, existing black-box predictive models are not readily suited for analyzing such questions. In this work, we develop an approximation method to compute the feature importance curves relevant to the unconditional distribution of outcomes, while leveraging the power of pre-trained black-box predictive models. The feature importance curves measure the changes across quantiles of outcome distribution given an external impact of change in the explanatory features. Through extensive numerical experiments and real data examples, we demonstrate that our approximation method produces sparse and faithful results, and is computationally efficient.

stat.ML↗

arXiv2024

Geometric constellation shaping for wireless optical intensity channels: An information-theoretic approach

A simple geometric shaping method is proposed for optical wireless communication systems based on intensity modulation and direct detection (IM/DD) from an information-theoretic perspective. Constellations consisting of equiprobable levels with exponential-like distribution are obtained, which possesses asymptotic optimality in the sense that the high-SNR capacity of average-intensity constrained optical intensity channel can be approached by such constellations with increasing size. All $2^b$ levels ($b\in\mathbb N$) of the obtained constellation can be represented by a basic level and $b+2$ bits, thereby reducing the required resolution of the digital-to-analog converter (DAC) without affecting the asymptotic optimality. Achievable information rate evaluations verify the asymptotic optimality. As an example, error performance results of a simple $16$-level LDPC coded modulation scheme show that a shaping gain of $0.65$ dB can be obtained by applying the proposed constellation design. This method can also be applied to more specific IM/DD channel models, since it only requires a near-optimal continuous input distribution.

cs.IT↗

arXiv2024

Molecular gas mass measurements of an active, starburst galaxy at $z\approx2.6$ using ALMA observations of the [CI], CO and dust emission

We present new ALMA observations of a starburst galaxy at cosmic noon hosting a radio-loud active galactic nucleus: PKS 0529-549 at $z=2.57$. To investigate the conditions of its cold interstellar medium, we use ALMA observations which spatially resolve the [CI] fine-structure lines, [CI] (2-1) and [CI] (1-0), CO rotational lines, CO (7-6) and CO (4-3), and the rest-frame continuum emission at 461 and 809 GHz. The four emission lines display different morphologies, suggesting spatial variation in the gas excitation conditions. The radio jets have just broken out of the molecular gas but not through the more extended ionized gas halo. The [CI] (2-1) emission is more extended ($\approx8\,{\rm kpc}\times5\,{\rm kpc}$) than detected in previous shallower ALMA observations. The [CI] luminosity ratio implies an excitation temperature of $44\pm16$ K, similar to the dust temperature. Using the [CI] lines, CO (4-3), and 227 GHz dust continuum, we infer the mass of molecular gas $M_{\mathrm{mol}}$ using three independent approaches and typical assumptions in the literature. All approaches point to a massive molecular gas reservoir of about $10^{11}$ $M_{\odot}$, but the exact values differ by up to a factor of 4. Deep observations are critical in correctly characterizing the distribution of cold gas in high-redshift galaxies, and highlight the need to improve systematic uncertainties in inferring accurate molecular gas masses.

astro-ph.GA↗

arXiv2024

Adaptive Safety with Control Barrier Functions and Triggered Batch Least-Squares Identifier

In this paper, a triggered Batch Least-Squares Identifier (BaLSI) based adaptive safety control scheme is proposed for uncertain systems with potentially conflicting control objectives and safety constraints. A relaxation term is added to the Quadratic Programs (QP) combining the transformed Control Lyapunov Functions (CLFs) and Control Barrier Functions (CBFs), to mediate the potential conflict. The existing Lyapunov-based adaptive schemes designed to guarantee specific properties of the Lyapunov functions, may grow unboundedly under the effects of the relaxation term. The adaptive law is designed by processing system inputs and outputs, to avoid unbounded estimates and overparameterization problems in the existing results. A safetytriggered condition is presented, based on which the forward invariant property of the safe set is shown and Zeno behavior can be excluded. Simulation results are presented to demonstrate the effectiveness of the proposed adaptive control scheme.

eess.SY↗

arXiv2024

Nematic Superconductivity and Its Critical Vestigial Phases in the Quasi-crystal

We propose a general mechanism to realize nematic superconductivity (SC) and reveal its exotic vestigial phases in the quasi-crystal (QC). Starting from a Penrose Hubbard model, our microscopic studies suggest that the Kohn-Luttinger mechanism driven SC in the QC is usually gapless due to violation of Anderson's theorem, rendering that both chiral and nematic SCs are common. The nematic SC in the QC can support novel vestigial phases driven by pairing phase fluctuations above its $T_c$. Our combined renormalization group and Monte-Carlo studies provide a phase diagram in which, besides the conventional charge-4e SC, two critical vestigial phases emerge, i.e. the quasi-nematic (Q-N) SC and Q-N metal. In the two Q-N phases, the discrete lattice rotation symmetry is counter-intuitively ``quasi-broken'' with power-law decaying orientation correlation. They separate the phase diagram into various phases connected via Berezinskii-Kosterlitz-Thouless (BKT) transitions. These remarkable critical vestigial phases, which resemble the intermediate BKT phase in the $q$-state ($q\ge 5$) clock model, are consequence of the five- (or higher-) fold anisotropy field brought about by the unique QC symmetry, which are absent in conventional crystalline materials.

cond-mat.str-el↗

arXiv2024

Adaptive Fusion of Radiomics and Deep Features for Lung Adenocarcinoma Subtype Recognition

The most common type of lung cancer, lung adenocarcinoma (LUAD), has been increasingly detected since the advent of low-dose computed tomography screening technology. In clinical practice, pre-invasive LUAD (Pre-IAs) should only require regular follow-up care, while invasive LUAD (IAs) should receive immediate treatment with appropriate lung cancer resection, based on the cancer subtype. However, prior research on diagnosing LUAD has mainly focused on classifying Pre-IAs/IAs, as techniques for distinguishing different subtypes of IAs have been lacking. In this study, we proposed a multi-head attentional feature fusion (MHA-FF) model for not only distinguishing IAs from Pre-IAs, but also for distinguishing the different subtypes of IAs. To predict the subtype of each nodule accurately, we leveraged both radiomics and deep features extracted from computed tomography images. Furthermore, those features were aggregated through an adaptive fusion module that can learn attention-based discriminative features. The utility of our proposed method is demonstrated here by means of real-world data collected from a multi-center cohort.

eess.IV↗

arXiv2024

Leveraging Web-Crawled Data for High-Quality Fine-Tuning

Most large language models are fine-tuned using either expensive human-annotated data or GPT-4 generated data which cannot guarantee performance in certain domains. We argue that although the web-crawled data often has formatting errors causing semantic inaccuracies, it can still serve as a valuable source for high-quality supervised fine-tuning in specific domains without relying on advanced models like GPT-4. To this end, we create a paired training dataset automatically by aligning web-crawled data with a smaller set of high-quality data. By training a language model on this dataset, we can convert web data with irregular formats into high-quality ones. Our experiments show that training with the model-transformed data yields better results, surpassing training with only high-quality data by an average score of 9.4% in Chinese math problems. Additionally, our 7B model outperforms several open-source models larger than 32B and surpasses well-known closed-source models such as GPT-3.5, highlighting the efficacy of our approach.

cs.CL↗

arXiv2024

Three Dimensional Gravity and Schramm-Loewner Evolution

The partition function of three dimensional gravity in the quantum regime is dual to the Ising model when the central charge $c=1/2$. Mathematically, we show that the three dimensional gravity can be described by Schramm-Loewner Evolution(SLE) with certain $κ$. In fact, SLE depends on the parameter $κ$ which controls the diffusion of the Brownian motion. Each value of $c < 1$ corresponds to two values of $κ$, which may hint that the three dimensional gravity has two different phases at certain central charge c. Moreover, phase transition is also discussed in AdS and Ising model.

hep-th↗

arXiv2024

Revisiting Modularity Maximization for Graph Clustering: A Contrastive Learning Perspective

Graph clustering, a fundamental and challenging task in graph mining, aims to classify nodes in a graph into several disjoint clusters. In recent years, graph contrastive learning (GCL) has emerged as a dominant line of research in graph clustering and advances the new state-of-the-art. However, GCL-based methods heavily rely on graph augmentations and contrastive schemes, which may potentially introduce challenges such as semantic drift and scalability issues. Another promising line of research involves the adoption of modularity maximization, a popular and effective measure for community detection, as the guiding principle for clustering tasks. Despite the recent progress, the underlying mechanism of modularity maximization is still not well understood. In this work, we dig into the hidden success of modularity maximization for graph clustering. Our analysis reveals the strong connections between modularity maximization and graph contrastive learning, where positive and negative examples are naturally defined by modularity. In light of our results, we propose a community-aware graph clustering framework, coined MAGI, which leverages modularity maximization as a contrastive pretext task to effectively uncover the underlying information of communities in graphs, while avoiding the problem of semantic drift. Extensive experiments on multiple graph datasets verify the effectiveness of MAGI in terms of scalability and clustering performance compared to state-of-the-art graph clustering methods. Notably, MAGI easily scales a sufficiently large graph with 100M nodes while outperforming strong baselines.

cs.LG↗

arXiv2024

Gaussian Mixture Model with Rare Events

We study here a Gaussian Mixture Model (GMM) with rare events data. In this case, the commonly used Expectation-Maximization (EM) algorithm exhibits extremely slow numerical convergence rate. To theoretically understand this phenomenon, we formulate the numerical convergence problem of the EM algorithm with rare events data as a problem about a contraction operator. Theoretical analysis reveals that the spectral radius of the contraction operator in this case could be arbitrarily close to 1 asymptotically. This theoretical finding explains the empirical slow numerical convergence of the EM algorithm with rare events data. To overcome this challenge, a Mixed EM (MEM) algorithm is developed, which utilizes the information provided by partially labeled data. As compared with the standard EM algorithm, the key feature of the MEM algorithm is that it requires additionally labeled data. We find that MEM algorithm significantly improves the numerical convergence rate as compared with the standard EM algorithm. The finite sample performance of the proposed method is illustrated by both simulation studies and a real-world dataset of Swedish traffic signs.

stat.ME↗

arXiv2024

The CO-dark molecular gas in the cold HI arc

The CO-dark molecular gas (DMG), which refers to the molecular gas not traced by CO emission, is crucial for the evolution of the interstellar medium (ISM). While the gas properties of DMG have been widely explored in the Solar neighborhood, whether or not they are similar in the outer disk regions of the Milky Way is still not well understood. In this Letter, we confirm the existence of DMG toward a cold HI arc structure at 13 kpc away from the Galactic center with both OH emission and HI narrow self-absorption (HINSA). This is the first detection of HINSA in the outer disk region, in which the HINSA fraction ($N_{\rm HINSA}$/$N_{\rm H_2}$ = 0.022$\pm$0.011) is an order of magnitude higher than the average value observed in nearby evolved dark clouds, but is consistent with that of the early evolutionary stage of dark clouds. The inferred H$_2$ column density from both extinction and OH emission ($N_{\rm H_2} \approx 10^{20}$ cm$^{-2}$) is an order of magnitude higher than previously estimated. Although the ISM environmental parameters are expected to be different between the outer Galactic disk regions and the Solar neighborhood, we find that the visual extinction ($A_{\rm V}$ = 0.19$\pm$0.03 mag), H$_2$-gas density ($n_{\rm H_2} = 91\pm46$ cm$^{-3}$), and molecular fraction (58\%$\pm$28\%) of the DMG are rather similar to those of nearby diffuse molecular clouds. The existence of DMG associated with the expanding HI supershell supports a scenario where the expansion of supershells may trigger the formation of molecular clouds within a crossing timescale of the shock wave ($\sim$10$^6$ yr).

astro-ph.GA↗

arXiv2024

Geometrically-Shaped Constellation for Visible Light Communications at Short Blocklength

In this paper, we present a general framework of designing geometrically shaped constellations for short-packet visible light communications with a peak- and an average-intensity constraints. By leveraging tools from large deviation theory, we first characterize the second-order asymptotics of the optimal constellation shaping region under aforementioned intensity constraints, which serves as a good performance measure for the best geometric shaping in finite blocklength. To further incorporate a sufficiently large coding gain and a nearly-maximum shaping gain, we construct multidimensional constellations by the nested structure of Construction B lattices, where the constellation shaping is implemented by controlling the boundary of the embedded sublattice, i.e., a strategy called coarsely shaping and finely coding. Fast algorithms for constellation mapping and demodulation are presented as well. As an illustrative example, we present an energy-efficient $24$-dimensional constellation design based on the Leech lattice, whose superiority over existing constellation designs is verified by numerical results.

cs.IT↗

arXiv2024

A Semiparametric Gaussian Mixture Model for Chest CT-based 3D Blood Vessel Reconstruction

Computed tomography (CT) has been a powerful diagnostic tool since its emergence in the 1970s. Using CT data, three-dimensional (3D) structures of human internal organs and tissues, such as blood vessels, can be reconstructed using professional software. This 3D reconstruction is crucial for surgical operations and can serve as a vivid medical teaching example. However, traditional 3D reconstruction heavily relies on manual operations, which are time-consuming, subjective, and require substantial experience. To address this problem, we develop a novel semiparametric Gaussian mixture model tailored for the 3D reconstruction of blood vessels. This model extends the classical Gaussian mixture model by enabling nonparametric variations in the component-wise parameters of interest according to voxel positions. We develop a kernel-based expectation-maximization algorithm for estimating the model parameters, accompanied by a supporting asymptotic theory. Furthermore, we propose a novel regression method for optimal bandwidth selection. Compared to the conventional cross-validation-based (CV) method, the regression method outperforms the CV method in terms of computational and statistical efficiency. In application, this methodology facilitates the fully automated reconstruction of 3D blood vessel structures with remarkable accuracy.

stat.AP↗

arXiv2024

A Selective Review on Statistical Methods for Massive Data Computation: Distributed Computing, Subsampling, and Minibatch Techniques

This paper presents a selective review of statistical computation methods for massive data analysis. A huge amount of statistical methods for massive data computation have been rapidly developed in the past decades. In this work, we focus on three categories of statistical computation methods: (1) distributed computing, (2) subsampling methods, and (3) minibatch gradient techniques. The first class of literature is about distributed computing and focuses on the situation, where the dataset size is too huge to be comfortably handled by one single computer. In this case, a distributed computation system with multiple computers has to be utilized. The second class of literature is about subsampling methods and concerns about the situation, where the sample size of dataset is small enough to be placed on one single computer but too large to be easily processed by its memory as a whole. The last class of literature studies those minibatch gradient related optimization techniques, which have been extensively used for optimizing various deep learning models.

stat.ME↗

arXiv2024

The potential of QQQ in the anisotropic background

In this work, we use the AdS/CFT correspondence to study the behavior of a triply heavy baryon within anisotropic backgrounds. Beginning with the total action of the three quarks, we derive the balance equation for the three-quark system and compute the separation distance and potential energy. Our results reveal a consistent decrease in both the separation distance and potential energy for the A configuration and the B configuration as the anisotropy coefficient $a$ increases. This suggests that the presence of an anisotropic background promotes the dissolution of the three-quark system. Additionally, we compare the potential energies of the A and B configurations and observe that the A configuration has a slightly smaller potential energy, suggesting greater stability compared to the B configuration.

hep-ph↗

arXiv2024

Target Speaker Extraction by Directly Exploiting Contextual Information in the Time-Frequency Domain

In target speaker extraction, many studies rely on the speaker embedding which is obtained from an enrollment of the target speaker and employed as the guidance. However, solely using speaker embedding may not fully utilize the contextual information contained in the enrollment. In this paper, we directly exploit this contextual information in the time-frequency (T-F) domain. Specifically, the T-F representations of the enrollment and the mixed signal are interacted to compute the weighting matrices through an attention mechanism. These weighting matrices reflect the similarity among different frames of the T-F representations and are further employed to obtain the consistent T-F representations of the enrollment. These consistent representations are served as the guidance, allowing for better exploitation of the contextual information. Furthermore, the proposed method achieves the state-of-the-art performance on the benchmark dataset and shows its effectiveness in the complex scenarios.

eess.AS↗