arXiv subjects

Xu Zhang

Publications and source records attributed to Xu Zhang.

At least 307 records · Page 17Linked to original sources

arXiv2024

Dynamically generated states from the $ηK^\bar{K}^$, $πK^\bar{K}^$, and $K K^\bar{K}^$ systems within the fixed-center approximation

The three-body systems $ηK^* \bar{K}^*$, $πK^* \bar{K}^*$, and $K K^* \bar{K}^*$ are further investigated within the framework of fixed-center approximation, where $K^* \bar{K}^*$ is treated as the fixed-center, corresponding to the possible scalar meson $a_0(1780)$ or the tensor meson $f_2'(1525)$. The interactions between $η$, $π$, $K$, and $K^*$ are taken from the chiral unitary approach. The resonance structures appear in the modulus squared of the three-body scattering amplitude and suggest that $η/ π/ K$-$(K^*\bar{K}^*)_{a_0(1780)/f'_2(1525)}$ hadron state can be formed. By scattering the $η$ meson on the fixed-center $(K^* \bar{K}^*)_{a_0(1780)}$, it is found that there is a distinct peak around 2100 MeV, as shown in the modulus squared of the three-body scattering amplitude, which can be associated with the meson $π(2070)$. For the scattering of the $η$ meson on the $(K^* \bar{K}^*)_{f_2'(1525)}$, a resonance structure around 1890 MeV is found and it can be associated with the $η_2(1870)$ meson. Other resonance structures are also found and can be associated with $π_2(1880)$ and $η(2010)$.

hep-ph↗

arXiv2024

Existence of optimal pairs for optimal control problems with states constrained to Riemannian manifolds

In this paper, we investigate the existence of optimal pairs for optimal control problems with their states constrained pointwise to Riemannian manifolds. For this purpose, by means of the Riemannian geometric tool, we introduce a crucial Cesari-type property, which is an extension of the classical Cesari property (See [3, Definition 3.3, p. 51]) from the setting of Euclidean spaces to that of Riemannian manifolds. Moreover, we show the efficiency of our result by a concrete example.

math.OC↗

arXiv2024

Long-lived topological time-crystalline order on a quantum processor

Topologically ordered phases of matter elude Landau's symmetry-breaking theory, featuring a variety of intriguing properties such as long-range entanglement and intrinsic robustness against local perturbations. Their extension to periodically driven systems gives rise to exotic new phenomena that are forbidden in thermal equilibrium. Here, we report the observation of signatures of such a phenomenon -- a prethermal topologically ordered time crystal -- with programmable superconducting qubits arranged on a square lattice. By periodically driving the superconducting qubits with a surface-code Hamiltonian, we observe discrete time-translation symmetry breaking dynamics that is only manifested in the subharmonic temporal response of nonlocal logical operators. We further connect the observed dynamics to the underlying topological order by measuring a nonzero topological entanglement entropy and studying its subsequent dynamics. Our results demonstrate the potential to explore exotic topologically ordered nonequilibrium phases of matter with noisy intermediate-scale quantum processors.

quant-ph↗

arXiv2024

The JCMT Transient Survey: Six-Year Summary of 450/850\,$μ$m Protostellar Variability and Calibration Pipeline Version 2.0

The JCMT Transient Survey has been monitoring eight Gould Belt low-mass star-forming regions since December 2015 and six somewhat more distant intermediate-mass star-forming regions since February 2020 with SCUBA-2 on the JCMT at \ShortS and \LongS and with an approximately monthly cadence. We introduce our Pipeline v2 relative calibration procedures for image alignment and flux calibration across epochs, improving on our previous Pipeline v1 by decreasing measurement uncertainties and providing additional robustness. These new techniques work at both \LongS and \ShortNS, where v1 only allowed investigation of the \LongS data. Pipeline v2 achieves better than $0.5^{\prime\prime}$ relative image alignment, less than a tenth of the submillimeter beam widths. The v2 relative flux calibration is found to be 1\% at \LongS and $<5$\% at \ShortNS. The improvement in the calibration is demonstrated by comparing the two pipelines over the first four years of the survey and recovering additional robust variables with v2. Using the full six years of the Gould Belt survey the number of robust variables increases by 50\,\%, and at \ShortS we identify four robust variables, all of which are also robust at \LongNS. The multi-wavelength light curves for these sources are investigated and found to be consistent with the variability being due to dust heating within the envelope in response to accretion luminosity changes from the central source.

astro-ph.IM↗

arXiv2024

Understanding LLMs: A Comprehensive Overview from Training to Inference

The introduction of ChatGPT has led to a significant increase in the utilization of Large Language Models (LLMs) for addressing downstream tasks. There's an increasing focus on cost-efficient training and deployment within this context. Low-cost training and deployment of LLMs represent the future development trend. This paper reviews the evolution of large language model training techniques and inference deployment technologies aligned with this emerging trend. The discussion on training includes various aspects, including data preprocessing, training architecture, pre-training tasks, parallel training, and relevant content related to model fine-tuning. On the inference side, the paper covers topics such as model compression, parallel computation, memory scheduling, and structural optimization. It also explores LLMs' utilization and provides insights into their future development.

cs.CL↗

arXiv2024

Plug-in Diffusion Model for Sequential Recommendation

Pioneering efforts have verified the effectiveness of the diffusion models in exploring the informative uncertainty for recommendation. Considering the difference between recommendation and image synthesis tasks, existing methods have undertaken tailored refinements to the diffusion and reverse process. However, these approaches typically use the highest-score item in corpus for user interest prediction, leading to the ignorance of the user's generalized preference contained within other items, thereby remaining constrained by the data sparsity issue. To address this issue, this paper presents a novel Plug-in Diffusion Model for Recommendation (PDRec) framework, which employs the diffusion model as a flexible plugin to jointly take full advantage of the diffusion-generating user preferences on all items. Specifically, PDRec first infers the users' dynamic preferences on all items via a time-interval diffusion model and proposes a Historical Behavior Reweighting (HBR) mechanism to identify the high-quality behaviors and suppress noisy behaviors. In addition to the observed items, PDRec proposes a Diffusion-based Positive Augmentation (DPA) strategy to leverage the top-ranked unobserved items as the potential positive samples, bringing in informative and diverse soft signals to alleviate data sparsity. To alleviate the false negative sampling issue, PDRec employs Noise-free Negative Sampling (NNS) to select stable negative samples for ensuring effective model optimization. Extensive experiments and analyses on four datasets have verified the superiority of the proposed PDRec over the state-of-the-art baselines and showcased the universality of PDRec as a flexible plugin for commonly-used sequential encoders in different recommendation scenarios. The code is available in https://github.com/hulkima/PDRec.

cs.IR↗

arXiv2024

Learning Surface Scattering Parameters From SAR Images Using Differentiable Ray Tracing

Simulating high-resolution Synthetic Aperture Radar (SAR) images in complex scenes has consistently presented a significant research challenge. The development of a microwave-domain surface scattering model and its reversibility are poised to play a pivotal role in enhancing the authenticity of SAR image simulations and facilitating the reconstruction of target parameters. Drawing inspiration from the field of computer graphics, this paper proposes a surface microwave rendering model that comprehensively considers both Specular and Diffuse contributions. The model is analytically represented by the coherent spatially varying bidirectional scattering distribution function (CSVBSDF) based on the Kirchhoff approximation (KA) and the perturbation method (SPM). And SAR imaging is achieved through the synergistic combination of ray tracing and fast mapping projection techniques. Furthermore, a differentiable ray tracing (DRT) engine based on SAR images was constructed for CSVBSDF surface scattering parameter learning. Within this SAR image simulation engine, the use of differentiable reverse ray tracing enables the rapid estimation of parameter gradients from SAR images. The effectiveness of this approach has been validated through simulations and comparisons with real SAR images. By learning the surface scattering parameters, substantial enhancements in SAR image simulation performance under various observation conditions have been demonstrated.

cs.CV↗

arXiv2023

Surveys of clumps, cores, and condensations in Cygnus-X:Searching for circumstellar disks

To investigate whether disk-mediated accretion is the primary mechanism in high-mass star formation, we have established a survey of a large sample of massive dense cores within a giant molecular cloud. We used high angular resolution ($\sim 1.8''$) observations with SMA to study the dust emission and molecular line emission of about 50 massive dense cores in Cygnus-X. At a typical distance of 1.4 kpc for Cygnus-X, these massive dense cores are resolved into $\sim 2000$ au condensations. We combined the CO outflow emission and gas kinematics traced by several high-density tracers to search for disk candidates. We extracted hundreds of dust condensations from the SMA 1.3 mm dust continuum emission. The CO data show bipolar or unipolar outflow signatures toward 49 dust condensations. Among them, only 27 sources are detected in dense gas tracers, which reveals the gas kinematics, and nine sources show evidence of rotating envelopes, suggesting the existence of embedded accretion disks. The position-velocity diagrams along the velocity gradient of all rotating condensations suggest that four condensations are possible to host Keplerian-like disks. A detailed investigation of the 27 sources detected in dense gas tracers suggests that the nine disk candidates are at earlier evolutionary stages compared to the remaining 18 sources. Non-detection of rotating disks in our sample may be due to several factors, including an unknown inclination angle of the rotation axis and an early evolutionary stage of the central source, and the latter could be important, considering that young and powerful outflows could confuse the observational evidence for rotation. The detection rate of disk candidates in our sample is 1/3, which confirms that disk accretion is a viable mechanism for high-mass star formation, although it may not be the only one.

astro-ph.GA↗

arXiv2023

Negative Pre-aware for Noisy Cross-modal Matching

Cross-modal noise-robust learning is a challenging task since noisy correspondence is hard to recognize and rectify. Due to the cumulative and unavoidable negative impact of unresolved noise, existing methods cannot maintain a stable performance when the noise increases. In this paper, we present a novel Negative Pre-aware Cross-modal (NPC) matching solution for large visual-language model fine-tuning on noisy downstream tasks. It is featured in two aspects: (1) For noise recognition and resistance, previous methods usually directly filter out a noise subset, we propose to estimate the negative impact of each sample. It does not need additional correction mechanisms that may predict unreliable correction results, leading to self-reinforcing error. We assign a confidence weight to each sample according to its negative impact in the training process. This adaptively adjusts the contribution of each sample to avoid noisy accumulation. (2) For maintaining stable performance with increasing noise, we utilize the memorization effect of DNNs by maintaining a memory bank. Specifically, we apply GMM to select high-confident clean samples as the memory entry, where the memory entry is used to estimate the negative impact of each sample. Since clean samples are easier distinguished by GMM with increasing noise, the memory bank can still maintain high quality at a high noise ratio. Compared to the correction mechanism focusing on noise samples, memory bank-based estimation is more robust, which makes the model performance stable on noisy datasets. Extensive experiments demonstrate that our method significantly improves matching accuracy and performance stability at increasing noise ratio. Our approach also surpasses the state-of-the-art methods by a large margin. The code is available at: https://github.com/ZhangXu0963/NPC.

cs.CV↗

arXiv2023

Independent-Set Design of Experiments for Estimating Treatment and Spillover Effects under Network Interference

Interference is ubiquitous when conducting causal experiments over networks. Except for certain network structures, causal inference on the network in the presence of interference is difficult due to the entanglement between the treatment assignments and the interference levels. In this article, we conduct causal inference under interference on an observed, sparse but connected network, and we propose a novel design of experiments based on an independent set. Compared to conventional designs, the independent-set design focuses on an independent subset of data and controls their interference exposures through the assignments to the rest (auxiliary set). We provide a lower bound on the size of the independent set from a greedy algorithm , and justify the theoretical performance of estimators under the proposed design. Our approach is capable of estimating both spillover effects and treatment effects. We justify its superiority over conventional methods and illustrate the empirical performance through simulations.

stat.ME↗

arXiv2023

Layer-dependent superconductivity in iron-based superconductors

The Hohenberg-Mermin-Wagner theorem states that a two-dimensional system cannot spontaneously break a continuous symmetry at finite temperature. This is supported by the observation of layer-dependent superconductivity in the quasi-two-dimensional superconductor NbSe2, in which the superconducting transition temperature (Tc) is reduced by about 60% in the monolayer limit. However, for the extremely anisotropic copper-based high-Tc superconductor Bi2Sr2CaCu2O8+δ (Bi-2212), the Tc of the monolayer is almost identical to that of its bulk counterpart. To clarify the effect of dimensionality on superconductivity, here we successfully fabricate ultrathin flakes of CsCa2Fe4As4F2, a highly anisotropic iron-based high-Tc superconductor, down to monolayer. The monolayer flake exhibits the highest Tc of 24 K (after tuning to the optimal doping by ionic liquid gating), which is about 20% lower than that of the bulk crystal. We also fabricate ultrathin flakes of CaKFe4As4, another iron-based superconductor with much smaller anisotropy. The Tc of the 3-layer flake decreases by 46%, showing a more pronounced dimensional effect than that of CsCa2Fe4As4F2. By carefully examining their anisotropy and the c-axis coherence length, we reveal the general trend and empirical law of the layer-dependent superconductivity in these quasi-two-dimensional superconductors. From this, the Tc of a new monolayer superconductor can be extrapolated.

cond-mat.supr-con↗

arXiv2023

MulMarker: a comprehensive framework for identifying multi-gene prognostic signatures

Prognostic signatures play an important role in clinical research, offering insights into the potential health outcomes of patients and guiding therapeutic decisions. Although single-gene prognostic biomarkers are valuable, multi-gene prognostic signatures offer deeper insights into disease progression. In this paper, we propose MulMarker, a comprehensive framework for identifying multi-gene prognostic signatures across various diseases. MulMarker comprises three core modules: a chatbot for addressing user queries, a module for identifying multi-gene prognostic signatures, and a module for generating tailored reports. Employing MulMarker, we identified a cell cycle-related prognostic signature that consists of CCNA1/2, CCNB1/2/3, CCNC, CCND1/2/3, CCNE1/2, CCNF, CCNG1/2, and CCNH. Based on the prognostic signature, we successfully stratified patients into high-risk and low-risk groups. Notably, our analysis revealed that patients in the low-risk group demonstrated a significantly higher survival rate compared to those in the high-risk group. Overall, MulMarker represents an efficient approach for the identification of multi-gene prognostic signatures. We release the code of MulMarker at https://github.com/Tina9/MulMarker.

q-bio.QM↗

arXiv2023

VidPlat: A Tool for Fast Crowdsourcing of Quality-of-Experience Measurements

For video or web services, it is crucial to measure user-perceived quality of experience (QoE) at scale under various video quality or page loading delays. However, fast QoE measurements remain challenging as they must elicit subjective assessment from human users. Previous work either (1) automates QoE measurements by letting crowdsourcing raters watch and rate QoE test videos or (2) dynamically prunes redundant QoE tests based on previously collected QoE measurements. Unfortunately, it is hard to combine both ideas because traditional crowdsourcing requires QoE test videos to be pre-determined before a crowdsourcing campaign begins. Thus, if researchers want to dynamically prune redundant test videos based on other test videos' QoE, they are forced to launch multiple crowdsourcing campaigns, causing extra overheads to re-calibrate or train raters every time. This paper presents VidPlat, the first open-source tool for fast and automated QoE measurements, by allowing dynamic pruning of QoE test videos within a single crowdsourcing task. VidPlat creates an indirect shim layer between researchers and the crowdsourcing platforms. It allows researchers to define a logic that dynamically determines which new test videos need more QoE ratings based on the latest QoE measurements, and it then redirects crowdsourcing raters to watch QoE test videos dynamically selected by this logic. Other than having fewer crowdsourcing campaigns, VidPlat also reduces the total number of QoE ratings by dynamically deciding when enough ratings are gathered for each test video. It is an open-source platform that future researchers can reuse and customize. We have used VidPlat in three projects (web loading, on-demand video, and online gaming). We show that VidPlat can reduce crowdsourcing cost by 31.8% - 46.0% and latency by 50.9% - 68.8%.

cs.HC↗

arXiv2023

Holistic Evaluation of GPT-4V for Biomedical Imaging

In this paper, we present a large-scale evaluation probing GPT-4V's capabilities and limitations for biomedical image analysis. GPT-4V represents a breakthrough in artificial general intelligence (AGI) for computer vision, with applications in the biomedical domain. We assess GPT-4V's performance across 16 medical imaging categories, including radiology, oncology, ophthalmology, pathology, and more. Tasks include modality recognition, anatomy localization, disease diagnosis, report generation, and lesion detection. The extensive experiments provide insights into GPT-4V's strengths and weaknesses. Results show GPT-4V's proficiency in modality and anatomy recognition but difficulty with disease diagnosis and localization. GPT-4V excels at diagnostic report generation, indicating strong image captioning skills. While promising for biomedical imaging AI, GPT-4V requires further enhancement and validation before clinical deployment. We emphasize responsible development and testing for trustworthy integration of biomedical AGI. This rigorous evaluation of GPT-4V on diverse medical images advances understanding of multimodal large language models (LLMs) and guides future work toward impactful healthcare applications.

eess.IV↗

arXiv2023

Brochosome-inspired binary metastructures for pixel-by-pixel thermal signature control

In nature, nano/micro-structured materials are adopted by various species to generate colors or achieve camouflage. Here, inspired by leafhopper-generated brochosomes, we design binary metastructures composed of pixel twins to achieve pixelated thermal signature control at the microscale. In the infrared range, the pixel twins exhibit distinct emissivities, creating thermal counterparts of '0-1' binary states for storing and displaying information. In the visible range, the engineered surface morphology of the pixel twins enables similar scattering behaviors, rendering the pixel twins visually indistinguishable, which achieves the camouflage of stored information. Compared to the previous work based on plasmonic holographic metasurfaces, the brochosome-like pixel twins are thermally driven, and their structure-enabled functions do not rely on permittivities of any specific material. The unique combination of visible camouflage and infrared display provides a systemic solution to the spatial control of thermal signatures, and has important implications for optical security, anticounterfeiting, and data encryption.

physics.optics↗

arXiv2023

An Empirical Study of Instruction-tuning Large Language Models in Chinese

The success of ChatGPT validates the potential of large language models (LLMs) in artificial general intelligence (AGI). Subsequently, the release of LLMs has sparked the open-source community's interest in instruction-tuning, which is deemed to accelerate ChatGPT's replication process. However, research on instruction-tuning LLMs in Chinese, the world's most spoken language, is still in its early stages. Therefore, this paper makes an in-depth empirical study of instruction-tuning LLMs in Chinese, which can serve as a cookbook that provides valuable findings for effectively customizing LLMs that can better respond to Chinese instructions. Specifically, we systematically explore the impact of LLM bases, parameter-efficient methods, instruction data types, which are the three most important elements for instruction-tuning. Besides, we also conduct experiment to study the impact of other factors, e.g., chain-of-thought data and human-value alignment. We hope that this empirical study can make a modest contribution to the open Chinese version of ChatGPT. This paper will release a powerful Chinese LLMs that is comparable to ChatGLM. The code and data are available at https://github.com/PhoebusSi/Alpaca-CoT.

cs.CL↗

arXiv2023

Multigap nodeless superconductivity in the topological semimetal PdTe

Recently PdTe was identified as a spin-orbit coupled topological Dirac semimetal and was claimed to exhibit both bulk-nodal and surface-nodeless superconducting gaps. Here we report the ultralow-temperature thermal conductivity measurements on PdTe single crystals with $T_c$ = 4.5 K to investigate its superconducting gap structure. It is found that the residual linear term $κ_0/T$ is negligible in zero magnetic field. Furthermore, the field dependence of $κ_0(H)/T$ exhibits an $\sf S$-shaped curve. These results suggest that PdTe has multiple nodeless superconducting gaps, which is at odds with the claimed bulk-nodal gap. The reason for the discrepancy is likely that previous angle-resolved photoemission spectroscopy measurements were only performed down to 2 K and cannot observe the smaller nodeless gap. The fully gapped superconducting state in PdTe is compatible with it being a topological superconductor candidate.

cond-mat.supr-con↗

arXiv2023

The solvability and a Stackelberg-Nash game problem for degenerate elliptic equations

This study aims to investigate the functional properties of weak solution spaces and their compact embedding properties in relation to the Dirichlet problem associated with a specific class of degenerate elliptic equations. To expand the scope of analysis for degenerate elliptic problems, we employ weighted Sobolev inequalities and compact embedding techniques within the framework of weighted Sobolev spaces. Additionally, we apply these findings to examine a Stackelberg-Nash game problem and obtain the existence of the Stackelberg-Nash equilibrium.

math.AP↗