arXiv subjects

Gang Chen

Publications and source records attributed to Gang Chen.

At least 199 records · Page 11Linked to original sources

arXiv2024

Precision measurement for open systems by non-hermitian linear response

The lower bound of estimated precision for a coherent parameter unitarily encoded in closed systems has been obtained, and such a lower bound is inversely proportional to the fluctuation of the encoding operator. In this paper, we first derive some general results regarding the lower bound of estimated precision for a dissipative parameter, which is non-unitarily encoded in open systems, by combining the law of error propagation and the non-hermitian linear response theory. This lower bound is related to the correlation of the encoding dissipative operator and the evolution time. We next demonstrate the utility of our general results by considering three different kinds of non-unitary encoding processes, including particle loss, relaxation, and dephasing. We finally compare the lower bound with the quantum Fisher information obtained by tomography and find they are consistent in the regime where the non-hermitian linear response applies. This lower bound can guide us to find the optimal initial states and detecting operators to significantly simplify the measurement process.

cond-mat.quant-gas↗

arXiv2024

Adverse Weather Optical Flow: Cumulative Homogeneous-Heterogeneous Adaptation

Optical flow has made great progress in clean scenes, while suffers degradation under adverse weather due to the violation of the brightness constancy and gradient continuity assumptions of optical flow. Typically, existing methods mainly adopt domain adaptation to transfer motion knowledge from clean to degraded domain through one-stage adaptation. However, this direct adaptation is ineffective, since there exists a large gap due to adverse weather and scene style between clean and real degraded domains. Moreover, even within the degraded domain itself, static weather (e.g., fog) and dynamic weather (e.g., rain) have different impacts on optical flow. To address above issues, we explore synthetic degraded domain as an intermediate bridge between clean and real degraded domains, and propose a cumulative homogeneous-heterogeneous adaptation framework for real adverse weather optical flow. Specifically, for clean-degraded transfer, our key insight is that static weather possesses the depth-association homogeneous feature which does not change the intrinsic motion of the scene, while dynamic weather additionally introduces the heterogeneous feature which results in a significant boundary discrepancy in warp errors between clean and degraded domains. For synthetic-real transfer, we figure out that cost volume correlation shares a similar statistical histogram between synthetic and real degraded domains, benefiting to holistically aligning the homogeneous correlation distribution for synthetic-real knowledge distillation. Under this unified framework, the proposed method can progressively and explicitly transfer knowledge from clean scenes to real adverse weather. In addition, we further collect a real adverse weather dataset with manually annotated optical flow labels and perform extensive experiments to verify the superiority of the proposed method.

cs.CV↗

arXiv2024

Hardware/Algorithm Co-design for Real-Time I/O Control with Improved Timing Accuracy and Robustness

In safety-critical systems, timing accuracy is the key to achieving precise I/O control. To meet such strict timing requirements, dedicated hardware assistance has recently been investigated and developed. However, these solutions are often fragile, due to unforeseen timing defects. In this paper, we propose a robust and timing-accurate I/O co-processor, which manages I/O tasks using Execution Time Servers (ETSs) and a two-level scheduler. The ETSs limit the impact of timing defects between tasks, and the scheduler prioritises ETSs based on their importance, offering a robust and configurable scheduling infrastructure. Based on the hardware design, we present an ETS-based timing-accurate I/O schedule, with the ETS parameters configured to further enhance robustness against timing defects. Experiments show the proposed I/O control method outperforms the state-of-the-art method in terms of timing accuracy and robustness without introducing significant overhead.

cs.AR↗

arXiv2024

Ideal flat and resolved SU(3) Landau levels in three dimensions

Landau levels (LLs) are of great importance for understanding the quantum Hall effect and associated many-body physics. Recently, their three-dimensional (3D) counterparts, i.e., dispersionless 3D LLs with well-defined quantum numbers, have attracted significant attention but have not yet been reported. Here we theoretically propose and experimentally observe 3D LLs with a sharply quantized spectrum in a diamond acoustic lattice, where the eigenstates are characterized by SU(3) quantum numbers. The engineered inhomogeneous hopping strengths not only introduce pseudomagnetic fields that quantize the nodal lines into LLs but also provide three bosonic degrees of freedom, embedding a generic SU(3) symmetry into the LLs. Using a phased array of acoustic sources, we selectively excite distinct eigenstates within the degenerate LL multiplets and visualize their 3D eigenmodes. Importantly, our approach enables the precise reconstruction of SU(3) quantum numbers directly from eigenmode correlations. Our results establish SU(3) LLs as a tractable model in artificial platforms, and pave the way for synthesizing LLs with zero dispersion and countable quantum numbers in arbitrary dimensions.

cond-mat.mes-hall↗

arXiv2024

Beyond the Carnot Limit in the Internal Cycles of a Quantum Heat Engine under Finite Heat Reservoirs

We investigate, in an analytical fashion, quantum Carnot cycles of a microscopic heat engine coupled to two nite heat reservoirs, whose internal cycles could own higher e ciency than the standard Carnot limit without consuming extra quantum resources, e.g., coherence or squeezing properties. The engine runs time-dependently, involving both the internal and external cycles to collaboratively accomplish a complete Carnot cycle, and the e ciency of the engine depends on the reservoirs heat capacities and the working substance. Our analytical results of the maximum efficiency and the maximum power output clarify the mechanism behind the high performance of the microscopic engines, displaying the key roles played by the nite-sized heat reservoirs. Our proposal is generally valid for any microscopic thermodynamic system and fully feasible under current laboratory conditions.

cond-mat.stat-mech↗

arXiv2024

Experimental Verification of Demon-Involved Fluctuation Theorems

The limit of energy saving in the control of small systems has recently attracted much interest due to the concept refinement of the Maxwell demon. Inspired by a newly proposed set of fluctuation theorems, we report the first experimental verification of these equalities and inequalities in a ultracold 40Ca ion system, confirming the intrinsic nonequilibrium in the system due to involvement of the demon. Based on elaborately designed demon-involved control protocols, such as the Szilard engine protocol, we provide experimentally quantitative evidence of the dissipative information, and observe tighter bounds of both the extracted work and the demon's efficacy than the limits predicted by the Sagawa-Ueda theorem. Our results substantiate a close connection between the physical nature of information and nonequilibrium processes at the microscale, which help further understanding the thermodynamic characteristics of information and the optimal design of nanoscale and smaller systems.

quant-ph↗

arXiv2024

Flavor Nernst effects in quantum paramagnets

Recent advances in spin transport research have highlighted the potential of quantum paramagnets as platforms for exploring novel phenomena and developing next-generation technologies. In this paper, we investigate the flavor Nernst effect (FNE) in quantum paramagnets, focusing on the Hall-type thermal spin transport of crystal electric field (CEF) excitations with spin-orbit couplings. As a proof of principle, we investigate the quantum paramagnetic ground state in an effective spin-1 Hamiltonian with Dzyaloshinskii-Moriya interactions and a large hard-axis anisotropy. We employ linear flavor-wave theory to analyze the low-energy excitations, and obtain the flavor Nernst coefficients from the linear response theory. We demonstrate the FNE in a 2D pyrochlore thin film with an all-in-all-out Ising axis configuration, and investigate their dependence on temperature, anisotropy, DM interaction, and external fields. Our results reveal the connection between the FNE and the Berry curvature of the CEF excitations, suggesting potential applications in manipulating thermal spin currents and exploring topological spin transport phenomena in quantum paramagnets.

cond-mat.str-el↗

arXiv2024

FreqINR: Frequency Consistency for Implicit Neural Representation with Adaptive DCT Frequency Loss

Recent advancements in local Implicit Neural Representation (INR) demonstrate its exceptional capability in handling images at various resolutions. However, frequency discrepancies between high-resolution (HR) and ground-truth images, especially at larger scales, result in significant artifacts and blurring in HR images. This paper introduces Frequency Consistency for Implicit Neural Representation (FreqINR), an innovative Arbitrary-scale Super-resolution method aimed at enhancing detailed textures by ensuring spectral consistency throughout both training and inference. During training, we employ Adaptive Discrete Cosine Transform Frequency Loss (ADFL) to minimize the frequency gap between HR and ground-truth images, utilizing 2-Dimensional DCT bases and focusing dynamically on challenging frequencies. During inference, we extend the receptive field to preserve spectral coherence between low-resolution (LR) and ground-truth images, which is crucial for the model to generate high-frequency details from LR counterparts. Experimental results show that FreqINR, as a lightweight approach, achieves state-of-the-art performance compared to existing Arbitrary-scale Super-resolution methods and offers notable improvements in computational efficiency. The code for our method will be made publicly available.

eess.IV↗

arXiv2024

Processing, evaluating and understanding FMRI data with afni_proc.py

FMRI data are noisy, complicated to acquire, and typically go through many steps of processing before they are used in a study or clinical practice. Being able to visualize and understand the data from the start through the completion of processing, while being confident that each intermediate step was successful, is challenging. AFNI's afni_proc$.$py is a tool to create and run a processing pipeline for FMRI data. With its flexible features, afni_proc$.$py allows users to both control and evaluate their processing at a detailed level. It has been designed to keep users informed about all processing steps: it does not just process the data, but first outputs a fully commented processing script that the users can read, query, interpret and refer back to. Having this full provenance is important for being able to understand each step of processing; it also promotes transparency and reproducibility by keeping the record of individual-level processing and modeling specifics in a single, shareable place. Additionally, afni_proc$.$py creates pipelines that contain several automatic self-checks for potential problems during runtime. The output directory contains a dictionary of relevant quantities that can be programmatically queried for potential issues and a systematic, interactive quality control (QC) HTML. All of these features help users evaluate and understand their data and processing in detail. We describe these and other aspects of afni_proc$.$py here using a set of task-based and resting state FMRI example commands.

q-bio.NC↗

arXiv2024

VecAug: Unveiling Camouflaged Frauds with Cohort Augmentation for Enhanced Detection

Fraud detection presents a challenging task characterized by ever-evolving fraud patterns and scarce labeled data. Existing methods predominantly rely on graph-based or sequence-based approaches. While graph-based approaches connect users through shared entities to capture structural information, they remain vulnerable to fraudsters who can disrupt or manipulate these connections. In contrast, sequence-based approaches analyze users' behavioral patterns, offering robustness against tampering but overlooking the interactions between similar users. Inspired by cohort analysis in retention and healthcare, this paper introduces VecAug, a novel cohort-augmented learning framework that addresses these challenges by enhancing the representation learning of target users with personalized cohort information. To this end, we first propose a vector burn-in technique for automatic cohort identification, which retrieves a task-specific cohort for each target user. Then, to fully exploit the cohort information, we introduce an attentive cohort aggregation technique for augmenting target user representations. To improve the robustness of such cohort augmentation, we also propose a novel label-aware cohort neighbor separation mechanism to distance negative cohort neighbors and calibrate the aggregated cohort information. By integrating this cohort information with target user representations, VecAug enhances the modeling capacity and generalization capabilities of the model to be augmented. Our framework is flexible and can be seamlessly integrated with existing fraud detection models. We deploy our framework on e-commerce platforms and evaluate it on three fraud detection datasets, and results show that VecAug improves the detection performance of base models by up to 2.48\% in AUC and 22.5\% in R@P$_{0.9}$, outperforming state-of-the-art methods significantly.

cs.LG↗

arXiv2024

Spinning waveforms in cubic effective field theories of gravity

We derive analytic all-order-in-spin expressions for the leading-order time-domain waveforms generated in the scattering of two Kerr black holes with arbitrary masses and spin vectors in the presence of all independent cubic deformations of Einstein-Hilbert gravity. These are the two parity-even interactions $I_1$ and $G_3$, and the parity-odd ones $\tilde{I}_1$ and $\tilde{G}_3$. Our results are obtained using three independent methods: a particularly efficient direct integration and tensor reduction approach; integration by parts combined with the method of differential equations; and finally a residue computation. For the case of the $G_3$ and $\tilde{G}_3$ deformations we can express the spinning waveform in terms of the scalar waveform with appropriately shifted impact parameters, which are reminiscent of Newman-Janis shifts. For $I_1$ and $\tilde{I}_1$ similar shifts occur, but are accompanied by additional contributions that cannot be captured by simply shifting the scalar $I_1$ and $\tilde{I}_1$ waveforms. We also show the absence of leading-order corrections to gravitational memory. Our analytic results are notably compact, and we compare the effectiveness of the three methods used to obtain them. We also briefly comment on the magnitude of the corrections to observables due to cubic deformations.

hep-th↗

arXiv2024

Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI

Machine learning (ML) on tabular data is ubiquitous, yet obtaining abundant high-quality tabular data for model training remains a significant obstacle. Numerous works have focused on tabular data augmentation (TDA) to enhance the original table with additional data, thereby improving downstream ML tasks. Recently, there has been a growing interest in leveraging the capabilities of generative AI for TDA. Therefore, we believe it is time to provide a comprehensive review of the progress and future prospects of TDA, with a particular emphasis on the trending generative AI. Specifically, we present an architectural view of the TDA pipeline, comprising three main procedures: pre-augmentation, augmentation, and post-augmentation. Pre-augmentation encompasses preparation tasks that facilitate subsequent TDA, including error handling, table annotation, table simplification, table representation, table indexing, table navigation, schema matching, and entity matching. Augmentation systematically analyzes current TDA methods, categorized into retrieval-based methods, which retrieve external data, and generation-based methods, which generate synthetic data. We further subdivide these methods based on the granularity of the augmentation process at the row, column, cell, and table levels. Post-augmentation focuses on the datasets, evaluation and optimization aspects of TDA. We also summarize current trends and future directions for TDA, highlighting promising opportunities in the era of generative AI. In addition, the accompanying papers and related resources are continuously updated and maintained in the GitHub repository at https://github.com/SuDIS-ZJU/awesome-tabular-data-augmentation to reflect ongoing advancements in the field.

cs.LG↗

arXiv2024

Color-Kinematic Numerators for Fermion Compton Amplitudes

We introduce a novel approach to compute Compton amplitudes involving a fermion pair inspired by Hopf algebra amplitude constructions. This approach features a recursive relation employing quasi-shuffle sets, directly verifiable by massive factorization properties. We derive results for minimal gauge invariant color-kinematic numerators with physical massive poles using this method. We have also deduced a graphical method for deriving numerators that simplifies the numerator generation and eliminates redundancies, thus providing several computational advantages.

hep-th↗

arXiv2024

Identifying an $\rm X(3872)$ tetraquark state versus a molecular state by formation time, velocity and temperature in relativistic nuclear collisions

The production of exotic hadron $\rm X(3872)$ in $pp$ collisions at $\sqrt{s}=2.76\,\mathrm{TeV}$ is investigated by the parton and hadron cascade model PACIAE in this work. In the simulation the final partonic state (quark matter, QM) and the final hadronic state (hadron matter, HM) are continuously processed and recorded. The $\rm X(3872)$ compact tetraquark state and loose molecular state are, respectively, coalesced and recombined in the QM and HM with the quantum statistical mechanics inspired dynamically constrained phase-space coalescence model. The formation time, velocity and temperature of QM (tetraquark state) and HM (molecular state) are proposed as identifying criteria between the two states. Our results in transverse momentum spectrum and rapidity distribution, etc. show a significant discrepancy between the two states and confirm that they are also valuable criteria identifying the $\rm X(3872)$ compact tetraquark state or molecular state.

hep-ph↗

arXiv2024

Static and multivariate-temporal attentive fusion transformer for readmission risk prediction

Background: Accurate short-term readmission prediction of ICU patients is significant in improving the efficiency of resource assignment by assisting physicians in making discharge decisions. Clinically, both individual static static and multivariate temporal data collected from ICU monitors play critical roles in short-term readmission prediction. Informative static and multivariate temporal feature representation capturing and fusion present challenges for accurate readmission prediction. Methods:We propose a novel static and multivariate-temporal attentive fusion transformer (SMTAFormer) to predict short-term readmission of ICU patients by fully leveraging the potential of demographic and dynamic temporal data. In SMTAFormer, we first apply an MLP network and a temporal transformer network to learn useful static and temporal feature representations, respectively. Then, the well-designed static and multivariate temporal feature fusion module is applied to fuse static and temporal feature representations by modeling intra-correlation among multivariate temporal features and constructing inter-correlation between static and multivariate temporal features. Results: We construct a readmission risk assessment (RRA) dataset based on the MIMIC-III dataset. The extensive experiments show that SMTAFormer outperforms advanced methods, in which the accuracy of our proposed method is up to 86.6%, and the area under the receiver operating characteristic curve (AUC) is up to 0.717. Conclusion: Our proposed SMTAFormer can efficiently capture and fuse static and multivariate temporal feature representations. The results show that SMTAFormer significantly improves the short-term readmission prediction performance of ICU patients through comparisons to strong baselines.

cs.LG↗

arXiv2024

NeurDB: An AI-powered Autonomous Data System

In the wake of rapid advancements in artificial intelligence (AI), we stand on the brink of a transformative leap in data systems. The imminent fusion of AI and DB (AIxDB) promises a new generation of data systems, which will relieve the burden on end-users across all industry sectors by featuring AI-enhanced functionalities, such as personalized and automated in-database AI-powered analytics, self-driving capabilities for improved system performance, etc. In this paper, we explore the evolution of data systems with a focus on deepening the fusion of AI and DB. We present NeurDB, an AI-powered autonomous data system designed to fully embrace AI design in each major system component and provide in-database AI-powered analytics. We outline the conceptual and architectural overview of NeurDB, discuss its design choices and key components, and report its current development and future plan.

cs.DB↗

arXiv2024

Quantum Spin Supersolid as a precursory Dirac Spin Liquid in a Triangular Lattice Antiferromagnet

Based on the recent experiments on the triangular lattice antiferromagnet Na$_2$BaCo(PO$_4$)$_2$, we propose the easy-axis XXZ spin-1/2 model on the triangular lattice, that exhibits a quantum spin supersolid, to be a precursory Dirac spin liquid. Despite the presence of a three-sublattice magnetic order as a spin supersolid, we suggest that this system is close to a Dirac spin liquid by exploring its spectroscopic response. The physical consequence is examined from the spectroscopic response, and we establish the continuous spectra near the M point in addition to the K point excitation from the spinon continuum on top of the three-sublattice order. Moreover, the satellite peaks were predicted at the mid-points connecting the $Γ$ and K points. This proposal offers a plausible understanding of the recent inelastic neutron scattering measurement in Na$_2$BaCo(PO$_4$)$_2$ and could inspire further research in relevant models and materials, such as K$_2$Co(SeO$_3$)$_2$ and Rb$_2$Co(SeO$_3$)$_2$, and even more anisotropic magnets like PrMgAl$_{11}$O$_{19}$.

cond-mat.str-el↗

arXiv2024

Is spontaneous vortex generation in superconducting 4Hb-TaS$_2$ from vison-vortex nucleation with $\mathbb{Z}_2$ topological order?

We propose the superconducting van der Waals material 4Hb-TaS$_2$ to realize the $\mathbb{Z}_2$ topological order and interpret the recent discovery of the spontaneous vortex generation in 4Hb-TaS$_2$ as the vison-vortex nucleation. For the alternating stacking of metallic/superconducting and Mott insulating layers in 4Hb-TaS$_2$, we expect the local moments in the Mott insulating 1T-TaS$_2$ layer to form the $\mathbb{Z}_2$ topological order. The spontaneous vortex generation in 4Hb-TaS$_2$ is interpreted from the transition or nucleation between the superconducting vortex and the $\mathbb{Z}_2$ vison in different phase regimes. Differing from the single vison-vortex nucleation in the original Senthil-Fisher's cuprate proposal, we consider such nucleation process between the superconducting vortex lattice and the vison crystal. We further propose experiments to distinguish this proposal with the $\mathbb{Z}_2$ topological order from the chiral spin liquid scenarios.

cond-mat.supr-con↗