arXiv subjects

Gang Li

Publications and source records attributed to Gang Li.

At least 235 records · Page 13Linked to original sources

arXiv2024

Generating entanglement of two acoustic modes by driving the qubit in circuit quantum acoustodynamics system

We propose how to generate the entanglement of two long-lived phonon modes in a circuit quantum acoustodynamics system, which consists of a multi-mode high-frequency bulk acoustic wave resonator and a transmon-type superconducting qubit. Two acoustic modes couple to the qubit through piezoelectric interaction, and the qubit is driven by a microwave field. Under the condition of far detuning between the qubit and acoustic modes, the qubit can be eliminated adiabatically, and thus establishing the indirect interaction between the two acoustic modes. We demonstrate that such the indirect interaction can be the parametric-amplification-type interaction by appropriately choosing the drive frequency and strength, so the entanglement between acoustic modes can be created by the direct unitary evolution. We numerically analyze the parameter conditions for generating the entanglement in detail and evaluate the influence of system dissipations and noise. The results show that the scheme can be realized using currently available parameters and has strong robustness against the dissipations and environmental temperature. This work may provide efficient resource for the quantum information processing based on the phononic systems.

quant-ph↗

arXiv2024

Beyond Thumbs Up/Down: Untangling Challenges of Fine-Grained Feedback for Text-to-Image Generation

Human feedback plays a critical role in learning and refining reward models for text-to-image generation, but the optimal form the feedback should take for learning an accurate reward function has not been conclusively established. This paper investigates the effectiveness of fine-grained feedback which captures nuanced distinctions in image quality and prompt-alignment, compared to traditional coarse-grained feedback (for example, thumbs up/down or ranking between a set of options). While fine-grained feedback holds promise, particularly for systems catering to diverse societal preferences, we show that demonstrating its superiority to coarse-grained feedback is not automatic. Through experiments on real and synthetic preference data, we surface the complexities of building effective models due to the interplay of model choice, feedback type, and the alignment between human judgment and computational interpretation. We identify key challenges in eliciting and utilizing fine-grained feedback, prompting a reassessment of its assumed benefits and practicality. Our findings -- e.g., that fine-grained feedback can lead to worse models for a fixed budget, in some settings; however, in controlled settings with known attributes, fine grained rewards can indeed be more helpful -- call for careful consideration of feedback attributes and potentially beckon novel modeling approaches to appropriately unlock the potential value of fine-grained feedback in-the-wild.

cs.LG↗

arXiv2024

RULE: Reliable Multimodal RAG for Factuality in Medical Vision Language Models

The recent emergence of Medical Large Vision Language Models (Med-LVLMs) has enhanced medical diagnosis. However, current Med-LVLMs frequently encounter factual issues, often generating responses that do not align with established medical facts. Retrieval-Augmented Generation (RAG), which utilizes external knowledge, can improve the factual accuracy of these models but introduces two major challenges. First, limited retrieved contexts might not cover all necessary information, while excessive retrieval can introduce irrelevant and inaccurate references, interfering with the model's generation. Second, in cases where the model originally responds correctly, applying RAG can lead to an over-reliance on retrieved contexts, resulting in incorrect answers. To address these issues, we propose RULE, which consists of two components. First, we introduce a provably effective strategy for controlling factuality risk through the calibrated selection of the number of retrieved contexts. Second, based on samples where over-reliance on retrieved contexts led to errors, we curate a preference dataset to fine-tune the model, balancing its dependence on inherent knowledge and retrieved contexts for generation. We demonstrate the effectiveness of RULE on medical VQA and report generation tasks across three datasets, achieving an average improvement of 47.4% in factual accuracy. We publicly release our benchmark and code in https://github.com/richard-peng-xia/RULE.

cs.LG↗

arXiv2024

The Theoretical Study of $Σ^{+} p \toΛa_0^{+} p$ Reaction

We conducted a theoretical study on the process $Σ^{+} p \toΛa_0^{+} p$ based on an effective Lagrangian approach. This model encompasses the excitation of intermediate states leading to the production of $Δ(1920)$ through $π^{+}$ and $K^{+}$ meson exchanges between the initial $Σ^{+}$ baryon and the initial proton $p$, as well as the generation of $Δ(1940)$ via $π^{+}$ and $ρ^{+}$ meson exchanges. We provide predictions for the total and differential cross sections and discuss the potential impacts of cutoff parameters, off-shell effects, and branching ratios on the $Δ^{\ast} \to a_0 p$ decay. Given the dominance of $Δ(1940)$ in the region close to the reaction threshold, this reaction is considered an ideal platform for deeply exploring the unique properties of the $Δ(1940)$ resonance. Through this approach, we can gain a more precise understanding of the intrinsic characteristics of this particle.

hep-ph↗

arXiv2024

Theoretical study on low-lying hidden-bottom and double-bottom tetraquark states

We perform a theoretical study on the spectrum of the low-lying hidden-bottom ($q\bar{q}b\bar{b}$ with $q=$ $u$, $d$ and $s$) and double-bottom ($q\bar{q}bb$) tetraquark states within a nonrelativistic quark model, in which the instanton-induced interaction is taken as the residual spin-dependent hyperfine interaction between quarks. All the model parameters are fixed by fitting the spectrum of the ground hadron states. The numerical results indicate that masses of several $X_{q\bar{q}}$, $Z_{b}$, and $Z_{bs}$ tetraquark states are below and near thresholds of corresponding meson-meson channels, thus these states may form components of exotic meson states with reasonable probabilities. Especially, in present model, masses of several obtained states with quantum number $I^G(J^P)=1^+(1^{+})$ are close to $Z_b^\pm(10610)$, so one may expect these states to be non-negligible components of the experimentally observed $Z_{b}$ states. Concerning to the double-bottom tetraquark states, the present results are in general consistent with other previous works. Two possible stable $T_{bb}$ states with quark content $bb\bar{n}\bar{n}$ ($n=u$ or $d$ quark) lying at energies $10558$ MeV and $10650$ MeV are found, and one possible stable $bb\bar{n}\bar{s}$ state is found, whose energy is $\sim10687$ MeV.

hep-ph↗

arXiv2024

Testable leptogenesis and $0νββ$ decay in extended seesaw model

We investigate the possibility of observable neutrinoless double beta decay $( 0 νββ)$ and viable leptogenesis within a low-scale extended inverse seesaw mechanism with additional sterile neutrinos. General effective field theory (EFT) considerations suggest that if there are experimentally observable signatures in $0 νββ$-decay and a lepton asymmetry generated by heavy right-handed neutrino decays, thermal leptogenesis is likely to be unviable. However, in this work, we show that in the context of low-scale leptogenesis, one can obtain the observed baryon asymmetry of the universe and observable signatures of $0 νββ$ decay in the presence of additional sterile neutrinos. In this framework, the light neutrino masses are suppressed by the extended seesaw parameter, $μ$, thereby allowing for $\mathcal{O}(10\, \mathrm{TeV})$ right-handed (RH) neutrinos, while avoiding small Yukawa couplings as in other neutrino mass models and near degeneracies in the RH neutrino spectrum as required by the low-scale leptogenesis paradigm. Contributions to the $0 νββ$-decay rate from additional sterile neutrinos can be appreciable, while the corresponding contributions to the early universe lepton asymmetry washout rate are suppressed by other parameters not entering the $0 νββ$-decay amplitudes. We show that for keV-MeV scale, sterile neutrinos future ton-scale $0νββ$-decay experiments offer potential signals while maintaining viable leptogenesis.

hep-ph↗

arXiv2024

Proposal of quantum repeater architecture based on Rydberg atom quantum processors

Realizing large-scale quantum networks requires the generation of high-fidelity quantum entanglement states between remote quantum nodes, a key resource for quantum communication, distributed computation and sensing applications. However, entanglement distribution between quantum network nodes is hindered by optical transmission loss and local operation errors. Here, we propose a novel quantum repeater architecture that synergistically integrates Rydberg atom quantum processors with optical cavities to overcome these challenges. Our scheme leverages cavity-mediated interactions for efficient remote entanglement generation, followed by Rydberg interaction-based entanglement purification and swapping. Numerical simulations, incorporating realistic experimental parameters, demonstrate the generation of Bell states with 99\% fidelity at rates of 1.1\,kHz between two nodes in local-area network (distance $0.1\,\mathrm{km}$), and can be extend to metropolitan-area ($25\,\mathrm{km}$) or intercity ($\mathrm{250\,\mathrm{km}}$, with the assitance of frequency converters) network with a rate of 0.1\,kHz. This scalable approach opens up near-term opportunities for exploring quantum network applications and investigating the advantages of distributed quantum information processing.

quant-ph↗

arXiv2024

GLRT-Based Metric Learning for Remote Sensing Object Retrieval

With the improvement in the quantity and quality of remote sensing images, content-based remote sensing object retrieval (CBRSOR) has become an increasingly important topic. However, existing CBRSOR methods neglect the utilization of global statistical information during both training and test stages, which leads to the overfitting of neural networks to simple sample pairs of samples during training and suboptimal metric performance. Inspired by the Neyman-Pearson theorem, we propose a generalized likelihood ratio test-based metric learning (GLRTML) approach, which can estimate the relative difficulty of sample pairs by incorporating global data distribution information during training and test phases. This guides the network to focus more on difficult samples during the training process, thereby encourages the network to learn more discriminative feature embeddings. In addition, GLRT is a more effective than traditional metric space due to the utilization of global data distribution information. Accurately estimating the distribution of embeddings is critical for GLRTML. However, in real-world applications, there is often a distribution shift between the training and target domains, which diminishes the effectiveness of directly using the distribution estimated on training data. To address this issue, we propose the clustering pseudo-labels-based fast parameter adaptation (CPLFPA) method. CPLFPA efficiently estimates the distribution of embeddings in the target domain by clustering target domain instances and re-estimating the distribution parameters for GLRTML. We reorganize datasets for CBRSOR tasks based on fine-grained ship remote sensing image slices (FGSRSI-23) and military aircraft recognition (MAR20) datasets. Extensive experiments on these datasets demonstrate the effectiveness of our proposed GLRTML and CPLFPA.

cs.CV↗

arXiv2024

Radial Evolution of ICME-Associated Particle Acceleration Observed by Solar Orbiter and ACE

On 2022 March 10, a coronal mass ejection (CME) erupted from the Sun, resulting in Solar Orbiter observations at 0.45 au of both dispersive solar energetic particles arriving prior to the interplanetary CME (ICME) and locally accelerated particles near the ICME-associated shock structure as it passed the spacecraft on 2022 March 11. This shock was later detected on 2022 March 14 by the Advanced Composition Explorer (ACE), which was radially aligned with Solar Orbiter, at 1 au. Ion composition data from both spacecraft -- via the Solar Orbiter Energetic Particle Detector/ Suprathermal Ion Spectrograph (EPD/SIS) and the Ultra Low Energy Isotope Spectrometer (ULEIS) on ACE -- allows for in-depth analysis of the radial evolution of species-dependent ICME shock-associated acceleration processes for this event. We present a study of the ion spectra observed at 0.45 and 1 au during both the gradual solar energetic particle (SEP) and energetic storm particle (ESP) phases of the event. We find that the shapes of the spectra seen at each spacecraft have significant differences that were likely caused by varying shock geometry: Solar Orbiter spectra tend to lack spectral breaks, and the higher energy portions of the ACE spectra have comparable average flux to the Solar Orbiter spectra. Through an analysis of rigidity effects on the spectral breaks observed by ACE, we conclude that the 1 au observations were largely influenced by a suprathermal pool of $\mathrm{He}^{+}$ ions that were enhanced due to propagation along a stream interaction region (SIR) that was interacting with the ICME at times of observation.

astro-ph.SR↗

arXiv2024

Deep Learning-based Automated Diagnosis of Obstructive Sleep Apnea and Sleep Stage Classification in Children Using Millimeter-wave Radar and Pulse Oximeter

Study Objectives: To evaluate the agreement between the millimeter-wave radar-based device and polysomnography (PSG) in diagnosis of obstructive sleep apnea (OSA) and classification of sleep stage in children. Methods: 281 children, aged 1 to 18 years, who underwent sleep monitoring between September and November 2023 at the Sleep Center of Beijing Children's Hospital, Capital Medical University, were recruited in the study. All enrolled children underwent sleep monitoring by PSG and the millimeter-wave radar-based device, QSA600, simultaneously. QSA600 recordings were automatically analyzed using a deep learning model meanwhile the PSG data was manually scored. Results: The Obstructive Apnea-Hypopnea Index (OAHI) obtained from QSA600 and PSG demonstrates a high level of agreement with an intraclass correlation coefficient of 0.945 (95% CI: 0.93 to 0.96). Bland-Altman analysis indicates that the mean difference of OAHI between QSA600 and PSG is -0.10 events/h (95% CI: -11.15 to 10.96). The deep learning model evaluated through cross-validation showed good sensitivity (81.8%, 84.3% and 89.7%) and specificity (90.5%, 95.3% and 97.1%) values for diagnosing children with OAHI>1, OAHI>5 and OAHI>10. The area under the receiver operating characteristic curve is 0.923, 0.955 and 0.988, respectively. For sleep stage classification, the model achieved Kappa coefficients of 0.854, 0.781, and 0.734, with corresponding overall accuracies of 95.0%, 84.8%, and 79.7% for Wake-sleep classification, Wake-REM-Light-Deep classification, and Wake-REM-N1-N2 N3 classification, respectively. Conclusions: QSA600 has demonstrated high agreement with PSG in diagnosing OSA and performing sleep staging in children. The device is portable, low-load and suitable for follow up and long-term pediatric sleep assessment.

eess.SP↗

arXiv2024

Exploring the Dynamics of CME-Driven Shocks by Comparing Numerical Modeling and Observations

Shocks driven by coronal mass ejections (CMEs) are primary drivers of gradual solar energetic particle (SEP) events, posing significant risks to space technology and astronauts. Concurrently, particles accelerated at these shocks may also propagate back to the Sun, potentially generating gamma-ray emissions through pion decay. We incorporated advanced modeling and multi-messenger observations to explore the role of CME-driven shocks in gamma-ray emissions and SEPs. Motivated by Fermi-LAT long-duration solar flares, we used the AWSoM MHD model to investigate the connection between the shocks and the properties of observed gamma-ray emissions. By coupling the AWSoM with iPATH model, we evaluate the impact of shock evolution complexity near the Sun on SEP intensity and spectra. Our result points to the importance of accurate background coronal and solar wind modeling, as well as detailed observations of CME source regions, in advancing our understanding of CME-driven shocks and the dynamics of associated energetic particles.

astro-ph.SR↗

arXiv2024

PDT: Uav Target Detection Dataset for Pests and Diseases Tree

UAVs emerge as the optimal carriers for visual weed iden?tification and integrated pest and disease management in crops. How?ever, the absence of specialized datasets impedes the advancement of model development in this domain. To address this, we have developed the Pests and Diseases Tree dataset (PDT dataset). PDT dataset repre?sents the first high-precision UAV-based dataset for targeted detection of tree pests and diseases, which is collected in real-world operational environments and aims to fill the gap in available datasets for this field. Moreover, by aggregating public datasets and network data, we further introduced the Common Weed and Crop dataset (CWC dataset) to ad?dress the challenge of inadequate classification capabilities of test models within datasets for this field. Finally, we propose the YOLO-Dense Pest (YOLO-DP) model for high-precision object detection of weed, pest, and disease crop images. We re-evaluate the state-of-the-art detection models with our proposed PDT dataset and CWC dataset, showing the completeness of the dataset and the effectiveness of the YOLO-DP. The proposed PDT dataset, CWC dataset, and YOLO-DP model are pre?sented at https://github.com/RuiXing123/PDT_CWC_YOLO-DP.

cs.CV↗

arXiv2024

FastGL: A GPU-Efficient Framework for Accelerating Sampling-Based GNN Training at Large Scale

Graph Neural Networks (GNNs) have shown great superiority on non-Euclidean graph data, achieving ground-breaking performance on various graph-related tasks. As a practical solution to train GNN on large graphs with billions of nodes and edges, the sampling-based training is widely adopted by existing training frameworks. However, through an in-depth analysis, we observe that the efficiency of existing sampling-based training frameworks is still limited due to the key bottlenecks lying in all three phases of sampling-based training, i.e., subgraph sample, memory IO, and computation. To this end, we propose FastGL, a GPU-efficient Framework for accelerating sampling-based training of GNN at Large scale by simultaneously optimizing all above three phases, taking into account both GPU characteristics and graph structure. Specifically, by exploiting the inherent overlap within graph structures, FastGL develops the Match-Reorder strategy to reduce the data traffic, which accelerates the memory IO without incurring any GPU memory overhead. Additionally, FastGL leverages a Memory-Aware computation method, harnessing the GPU memory's hierarchical nature to mitigate irregular data access during computation. FastGL further incorporates the Fused-Map approach aimed at diminishing the synchronization overhead during sampling. Extensive experiments demonstrate that FastGL can achieve an average speedup of 11.8x, 2.2x and 1.5x over the state-of-the-art frameworks PyG, DGL, and GNNLab, respectively.Our code is available at https://github.com/a1bc2def6g/fastgl-ae.

cs.LG↗

arXiv2024

Increased resistance to photooxidation in Dion-Jacobson lead halide perovskites -- implication for perovskite device stability

2D metal halide perovskites have enabled significant stability improvements in perovskite devices, particularly in resistance to moisture. However, some 2D perovskites are even more susceptible to photooxidation compared to 3D perovskites. This is particularly true for more commonly investigated Ruddlesden-Popper (RP) perovskites that exhibit increased susceptibility to photoinduced degradation compared to Dion-Jacobson (DJ) perovskites. Comparisons between different RP and DJ perovskites reveal that this phenomenon cannot be explained by commonly proposed differences in superoxide ion generation, interlayer distance and lattice structural rigidity differences. Instead, the resistance to photooxidation of DJ perovskites can be attributed to decreased likelihood of double deprotonation events (compared to single deprotonation events in RP perovskites) required for the loss of organic cations and the perovskite decomposition. Consequently, DJ perovskites are less susceptible to oxidative degradation (both photo- and electrochemically induced), which leads to improved operational stability of solar cells based on these materials.

cond-mat.mtrl-sci↗

arXiv2024

NN-Copula-CD: A Copula-Guided Interpretable Neural Network for Change Detection in Heterogeneous Remote Sensing Images

Change detection (CD) in heterogeneous remote sensing images has been widely used for disaster monitoring and land-use management. In the past decade, the heterogeneous CD problem has significantly benefited from the development of deep neural networks (DNNs). However, the purely data-driven DNNs perform like a black box where the lack of interpretability limits the trustworthiness and controllability of DNNs in most practical CD applications. As a powerful knowledge-driven tool, copula theory performs well in modeling relationships among random variables. To enhance the interpretability of existing neural networks for CD, we propose a knowledge-data-driven heterogeneous CD method based on a copula-guided neural network, named NN-Copula-CD. In our NN-Copula-CD, the mathematical characteristics of copula are employed as the loss functions to supervise a neural network to learn the dependence between bi-temporal heterogeneous superpixel pairs, and then the changed regions are identified via binary classification based on the degrees of dependence of all the superpixel pairs in the bi-temporal images. We conduct in-depth experiments on three datasets with heterogeneous images, where both quantitative and visual results demonstrate the effectiveness of our proposed NN-Copula-CD method.

cs.CV↗

arXiv2024

Evidence for field induced quantum spin liquid behavior in a spin-1/2 honeycomb magnet

One of the most important issues in modern condensed matter physics is the realization of fractionalized excitations, such as the Majorana excitations in the Kitaev quantum spin liquid. To this aim, the 3d-based Kitaev material Na2Co2TeO6 is a promising candidate whose magnetic phase diagram of B // a* contains a field-induced intermediate magnetically disordered phase within 7.5 T < |B| < 10 T. The experimental observations, including the restoration of the crystalline point group symmetry in the angle-dependent torque and the coexisting magnon excitations and spinon-continuum in the inelastic neutron scattering spectrum, provide strong evidence that this disordered phase is a field induced quantum spin liquid with partially polarized spins. Our variational Monte Carlo simulation with the effective K-J1-Γ-Γ'-J3 model reproduces the experimental data and further supports this conclusion.

cond-mat.str-el↗

arXiv2024

Pionic transitions of the spin-2 partner of $X(3872)$ to $χ_{cJ}$

We investigated the pionic transitions between the $X_2$ [spin-2 partner of the $X(3872)$] and $χ_{c1,2}$ using a nonrelativistic effective field theory. The $X_2$ is assumed to be a bound state of the $D^{*}$ and $\bar{D}^*$ mesons and to decay through several kinds of loops, including the bubble, triangle and box loops. Within the present model, the widths for the single-pion decays $X_2\toπ^0χ_{cJ}$ are predicted to be about $3$--$30$ keV. For the dipion decays, the widths are a few keVs. These widths yield a branching fraction of $10^{-3}$--$10^{-2}$. The ratio $R_{\mathrm{c}0}=Γ(X_2\toπ^+π^-χ_{cJ})/Γ(X_2\toπ^0π^0χ_{cJ}) \simeq 1.6$, which is a bit smaller than the expected value of $2$, and $R_{21}=Γ(X_2\toππχ_{c2})/Γ(X_2\toππχ_{c1}) \simeq 0.85$. These ratios are nearly independent of the $X_2$ mass and the coupling constants, which might be a good quantity for the experiments. Moreover, the invariant mass spectra of the $π^0χ_{cJ}$ final state for the dipion processes are presented, showing a cusp structure at the $D {\bar D}^*$ threshold enhanced and narrowed by the nearby triangle singularity.

hep-ph↗

arXiv2024

Leveraging Open Knowledge for Advancing Task Expertise in Large Language Models

The cultivation of expertise for large language models (LLMs) to solve tasks of specific areas often requires special-purpose tuning with calibrated behaviors on the expected stable outputs. To avoid huge cost brought by manual preparation of instruction datasets and training resources up to hundreds of hours, the exploitation of open knowledge including a wealth of low rank adaptation (LoRA) models and instruction datasets serves as a good starting point. However, existing methods on model and data selection focus on the performance of general-purpose capabilities while neglecting the knowledge gap exposed in domain-specific deployment. In the present study, we propose to bridge such gap by introducing few human-annotated samples (i.e., K-shot) for advancing task expertise of LLMs with open knowledge. Specifically, we develop an efficient and scalable pipeline to cost-efficiently produce task experts where K-shot data intervene in selecting the most promising expert candidates and the task-relevant instructions. A mixture-of-expert (MoE) system is built to make the best use of individual-yet-complementary knowledge between multiple experts. We unveil the two keys to the success of a MoE system, 1) the abidance by K-shot, and 2) the insistence on diversity. For the former, we ensure that models that truly possess problem-solving abilities on K-shot are selected rather than those blind guessers. Besides, during data selection, instructions that share task-relevant contexts with K-shot are prioritized. For the latter, we highlight the diversity of constituting experts and that of the fine-tuning instructions throughout the model and data selection process. Extensive experimental results confirm the superiority of our approach over existing methods on utilization of open knowledge across various tasks. Our codes will be available at https://github.com/Yaphabates/Rocket.

cs.CV↗