arXiv subjects

Zhen Zhang

Publications and source records attributed to Zhen Zhang.

At least 361 records · Page 20Linked to original sources

arXiv2023

Bayesian Inference of the Symmetry Energy and the Neutron Skin in $^{48}$Ca and $^{208}$Pb from CREX and PREX-2

Using the recent model-independent determination of the charge-weak form factor difference $ΔF_{\rm CW}$ in $^{48}$Ca and $^{208}$Pb by the CREX and PREX-2 collaborations together with some well-determined properties of doubly magic nuclei, we perform Bayesian inference of the symmetry energy $E_{\rm sym}(ρ)$ and the neutron skin thickness $Δr_{\rm np}$ of $^{48}$Ca and $^{208}$Pb within the Skyrme energy density functional (EDF). We find the inferred $E_{\rm sym}(ρ)$ and $Δr_{\rm np}$ separately from CREX and PREX-2 are compatible with each other at $90\%$ C.L., although they are inconsistent at $68.3\%$ C.L. with CREX (PREX-2) favoring a very soft (stiff) $E_{\rm sym}(ρ)$ and rather small (large) $Δr_{\rm np}$. By combining the CREX and PREX-2 data, we obtain a soft symmetry energy around saturation density $ρ_0$ and thinner $Δr_{\rm np}$ of $^{48}$Ca and $^{208}$Pb, which are found to be closer to the corresponding results from CREX alone, implying the PREX-2 is less effective to constrain the $E_{\rm sym}(ρ)$ and $Δr_{\rm np}$ due to its lower precision of $ΔF_{\rm CW}$. Furthermore, we find the Skyrme EDF results inferred by combining the CREX and PREX-2 data nicely agree with the measured dipole polarizabilities $α_D$ in $^{48}$Ca and $^{208}$Pb as well as the neutron matter equation of state from microscopic calculations. The implications of the inferred soft $E_{\rm sym}(ρ)$ around $ρ_0$ are discussed.

nucl-th↗

arXiv2023

Flares from merged magnetars: their prospects as a new population of gamma-ray counterparts of binary neutron star mergers

Long-lived massive magnetars are expected to be remnants of some binary neutron star (BNS) mergers. In this paper, we argue that the magnetic powered flaring activities of these merged magnetars would occur dominantly in their early millisecond-period-spin phase, which is in the timescale of days. Such flares endure significant absorption by the ejecta from the BNS collision, and their detectable energy range is from 0.1-10 MeV, in a time-lag of $\sim$ days after the merger events indicated by the gravitational wave chirps. We estimate the rate of such flares in different energy ranges, and find that there could have been ~0.1-10 cases detected by Fermi/GBM. A careful search for $\sim10$ milliseconds spin period modulation in weak short gamma-ray bursts (GRBs) may identify them from the archival data. The next generation MeV detectors could detect them at a mildly higher rate. The recent report on the Quasi-Period-Oscillation found in two BASTE GRBs should not be considered as cases of such flares, for they were detected in a lower energy range and with a much shorter period spin modulation.

astro-ph.HE↗

arXiv2023

Factor Graph Neural Networks

In recent years, we have witnessed a surge of Graph Neural Networks (GNNs), most of which can learn powerful representations in an end-to-end fashion with great success in many real-world applications. They have resemblance to Probabilistic Graphical Models (PGMs), but break free from some limitations of PGMs. By aiming to provide expressive methods for representation learning instead of computing marginals or most likely configurations, GNNs provide flexibility in the choice of information flowing rules while maintaining good performance. Despite their success and inspirations, they lack efficient ways to represent and learn higher-order relations among variables/nodes. More expressive higher-order GNNs which operate on k-tuples of nodes need increased computational resources in order to process higher-order tensors. We propose Factor Graph Neural Networks (FGNNs) to effectively capture higher-order relations for inference and learning. To do so, we first derive an efficient approximate Sum-Product loopy belief propagation inference algorithm for discrete higher-order PGMs. We then neuralize the novel message passing scheme into a Factor Graph Neural Network (FGNN) module by allowing richer representations of the message update rules; this facilitates both efficient inference and powerful end-to-end learning. We further show that with a suitable choice of message aggregation operators, our FGNN is also able to represent Max-Product belief propagation, providing a single family of architecture that can represent both Max and Sum-Product loopy belief propagation. Our extensive experimental evaluation on synthetic as well as real datasets demonstrates the potential of the proposed model.

cs.LG↗

arXiv2023

Individual and Averaged Power Density Spectra of X-ray bursts from SGR J1935+2154: Quasiperiodic Oscillation Search and Slopes

The study of quasi-periodic oscillations (QPOs) and power density spectra (PDS) continuum properties can help shed light on the still illusive emission physics of magnetars and as a window into the interiors of neutron stars using asteroseismology. In this work, we employ a Bayesian method to search for the QPOs in the hundreds of X-ray bursts from SGR J1935+2154 observed by {\it Insight}-HXMT, GECAM and Fermi/GBM from July 2014 to January 2022. Although no definitive QPO signal (significance $>3σ$) is detected in individual bursts or the averaged periodogram of the bursts grouped by duration, we identify several bursts exhibiting possible QPO at $\sim$ 40 Hz, which is consistent with that reported in the X-ray burst associated with FRB 200428. We investigate the PDS continuum properties and find that the distribution of the PDS slope in the simple power-law model peaks $\sim$ 2.5, which is consistent with other magnetars but higher than 5/3 commonly seen in gamma-ray bursts. Besides, the distribution of the break frequency in the broken power-law model peaks at $\sim$ 60 Hz. Finally, we report that the power-law index of PDS has an anti-correlation and power-law dependence on the burst duration as well as the minimum variation timescale.

astro-ph.HE↗

arXiv2023

Electrically tunable VO2-metal metasurface for mid-infrared switching, limiting, and nonlinear isolation

We demonstrate an electrically controlled metal-VO2 metasurface for the mid-wave infrared that simultaneously functions as a tunable optical switch, an optical limiter with a tunable limiting threshold, and a nonlinear optical isolator with a tunable operating range. The tunability is achieved via Joule heating through the metal comprising the metasurface, resulting in an integrated optoelectronic device. As an optical switch, the device has an experimental transmission ratio of ~100 when varying the bias current. Operating as an optical limiter, we demonstrated tunability of the limiting threshold from 20 mW to 180 mW of incident laser power. Similar degrees of tunability are also achieved for nonlinear optical isolation, which enables asymmetric (nonreciprocal) transmission.

physics.optics↗

arXiv2023

Active Q-switched X-Ray Regenerative Amplifier Free-Electron Laser

Despite tremendous progress in x-ray free-electron laser (FEL) science over the last decade, future applications still demand fully coherent, stable x-rays that have not been demonstrated in existing X-ray FEL facilities. In this Letter, we describe an active Q-switched x-ray regenerative amplifier FEL scheme to produce fully coherent, high-brightness, hard x rays at a high-repetition rate. By using simple electron-beam phase space manipulation, we show this scheme is flexible in controlling the x-ray cavity quality factor Q and hence the output radiation. We report both theoretical and numerical studies on this scheme with a wide range of accelerator, x-ray cavity, and undulator parameters.

physics.acc-ph↗

arXiv2023

Discovering a reaction-diffusion model for Alzheimer's disease by combining PINNs with symbolic regression

Misfolded tau proteins play a critical role in the progression and pathology of Alzheimer's disease. Recent studies suggest that the spatio-temporal pattern of misfolded tau follows a reaction-diffusion type equation. However, the precise mathematical model and parameters that characterize the progression of misfolded protein across the brain remain incompletely understood. Here, we use deep learning and artificial intelligence to discover a mathematical model for the progression of Alzheimer's disease using longitudinal tau positron emission tomography from the Alzheimer's Disease Neuroimaging Initiative database. Specifically, we integrate physics informed neural networks (PINNs) and symbolic regression to discover a reaction-diffusion type partial differential equation for tau protein misfolding and spreading. First, we demonstrate the potential of our model and parameter discovery on synthetic data. Then, we apply our method to discover the best model and parameters to explain tau imaging data from 46 individuals who are likely to develop Alzheimer's disease and 30 healthy controls. Our symbolic regression discovers different misfolding models $f(c)$ for two groups, with a faster misfolding for the Alzheimer's group, $f(c) = 0.23c^3 - 1.34c^2 + 1.11c$, than for the healthy control group, $f(c) = -c^3 +0.62c^2 + 0.39c$. Our results suggest that PINNs, supplemented by symbolic regression, can discover a reaction-diffusion type model to explain misfolded tau protein concentrations in Alzheimer's disease. We expect our study to be the starting point for a more holistic analysis to provide image-based technologies for early diagnosis, and ideally early treatment of neurodegeneration in Alzheimer's disease and possibly other misfolding-protein based neurodegenerative disorders.

cs.LG↗

arXiv2023

Certified Robustness for Large Language Models with Self-Denoising

Although large language models (LLMs) have achieved great success in vast real-world applications, their vulnerabilities towards noisy inputs have significantly limited their uses, especially in high-stake environments. In these contexts, it is crucial to ensure that every prediction made by large language models is stable, i.e., LLM predictions should be consistent given minor differences in the input. This largely falls into the study of certified robust LLMs, i.e., all predictions of LLM are certified to be correct in a local region around the input. Randomized smoothing has demonstrated great potential in certifying the robustness and prediction stability of LLMs. However, randomized smoothing requires adding noise to the input before model prediction, and its certification performance depends largely on the model's performance on corrupted data. As a result, its direct application to LLMs remains challenging and often results in a small certification radius. To address this issue, we take advantage of the multitasking nature of LLMs and propose to denoise the corrupted inputs with LLMs in a self-denoising manner. Different from previous works like denoised smoothing, which requires training a separate model to robustify LLM, our method enjoys far better efficiency and flexibility. Our experiment results show that our method outperforms the existing certification methods under both certified robustness and empirical robustness. The codes are available at https://github.com/UCSB-NLP-Chang/SelfDenoise.

cs.CL↗

arXiv2023

The Minimum Variation Timescales of X-ray bursts from SGR J1935+2154

The minimum variation timescale (MVT) of soft gamma-ray repeaters can be an important probe to estimate the emission region in pulsar-like models, as well as the Lorentz factor and radius of the possible relativistic jet in gamma-ray burst (GRB)-like models, thus revealing their progenitors and physical mechanisms. In this work, we systematically study the MVTs of hundreds of X-ray bursts (XRBs) from SGR J1935+2154 observed by {\it Insight}-HXMT, GECAM and Fermi/GBM from July 2014 to Jan 2022 through the Bayesian Block algorithm. We find that the MVTs peak at $\sim$ 2 ms, corresponding to a light travel time size of about 600 km, which supports the magnetospheric origin in pulsar-like models. The shock radius and the Lorentz factor of the jet are also constrained in GRB-like models. Interestingly, the MVT of the XRB associated with FRB 200428 is $\sim$ 70 ms, which is longer than that of most bursts and implies its special radiation mechanism. Besides, the median of MVTs is 7 ms, shorter than the median MVTs of 40 ms and 480 ms for short GRBs or long GRBs, respectively. However, the MVT is independent of duration, similar to GRBs. Finally, we investigate the energy dependence of MVT and suggest that there is a marginal evidence for a power-law relationship like GRBs but the rate of variation is at least about an order of magnitude smaller. These features may provide an approach to identify bursts with a magnetar origin.

astro-ph.HE↗

arXiv2023

Enhanced Contour Tracking: a Time-Varying Internal Model Principle-Based Approach

Contour tracking plays a crucial role in multi-axis motion control systems, and it requires both multi-axial contouring as well as standard servo performance in each axis. Among the existing contouring control methods, the cross coupled control (CCC) lacks of an asymptotical tracking performance for general contours, and the task coordinate frame (TCF) control usually leads to system nonlinearity, and by design is not well-suited for multi-axis contour tracking. Here we propose a novel time-varying internal model principle-based contouring control (TV-IMCC) methodology to enhance contour tracking performance with both axial and contour error reduction. The proposed TV-IMCC is twofold, including an extended position domain framework with master-slave structures for contour regulation, and a time-varying internal model principle-based controller for each axial tracking precision improvement. Specifically, a novel signal conversion algorithm is proposed with the extended position domain framework, hence the original n-axis contouring problem can be decoupled into (n-1) two-axis master-slave tracking problems in the position domain, and the class of contour candidates can be extended as well. With this, the time-varying internal model principle-based control method is proposed to deal with the time-varying dynamics in the axial systems resulted from the transformation between the time and position domains. Furthermore, the stability analysis is provided for the closed-loop system of the TV-IMCC. Various simulation and experimental results validate the TV-IMCC with enhanced contour tracking performance compared with the existing methods. Moreover, there is no strict requirement on the precision of the master axis, therefore a potential application of the TV-IMCC is multi-axis macro-micro motion systems.

eess.SY↗

arXiv2023

OpenDelta: A Plug-and-play Library for Parameter-efficient Adaptation of Pre-trained Models

The scale of large pre-trained models (PTMs) poses significant challenges in adapting to downstream tasks due to the high optimization overhead and storage costs associated with full-parameter fine-tuning. To address this, many studies explore parameter-efficient tuning methods, also framed as "delta tuning", which updates only a small subset of parameters, known as "delta modules", while keeping the backbone model's parameters fixed. However, the practicality and flexibility of delta tuning have been limited due to existing implementations that directly modify the code of the backbone PTMs and hard-code specific delta tuning methods for each PTM. In this paper, we present OpenDelta, an open-source library that overcomes these limitations by providing a plug-and-play implementation of various delta tuning methods. Our novel techniques eliminate the need to modify the backbone PTMs' code, making OpenDelta compatible with different, even novel PTMs. OpenDelta is designed to be simple, modular, and extensible, providing a comprehensive platform for researchers and practitioners to adapt large PTMs efficiently.

cs.LG↗

arXiv2023

Core States of Neutron Stars from Anatomizing their Scaled Structure Equations

Given an Equation of State (EOS) for neutron star (NS) matter, there is a unique mass-radius sequence characterized by a maximum mass $M_{\rm{NS}}^{\max}$ at radius $R_{\max}$. We first show analytically that the $M_{\rm{NS}}^{\max}$ and $R_{\max}$ scale linearly with two different combinations of NS central pressure $P_{\rm{c}}$ and energy density $\varepsilon_{\rm{c}}$ by dissecting perturbatively the dimensionless Tolman-Oppenheimer-Volkoff (TOV) equations governing NS internal variables. The scaling relations are then verified via 87 widely used and rather diverse phenomenological as well as 17 microscopic NS EOSs with/without considering hadron-quark phase transitions and hyperons by solving numerically the original TOV equations. The EOS of densest NS matter allowed before it collapses into a black hole (BH) is then obtained. Using the universal $M_{\rm{NS}}^{\max}$ and $R_{\max}$ scalings and NICER (Neutron Star Interior Composition Explorer) and XMM-Newton mass-radius observational data for PSR J0740+6620, a very narrow constraining band on the NS central EOS is extracted directly from the data for the first time without using any specific input EOS model.

nucl-th↗

arXiv2023

Uncertainty in Natural Language Processing: Sources, Quantification, and Applications

As a main field of artificial intelligence, natural language processing (NLP) has achieved remarkable success via deep neural networks. Plenty of NLP tasks have been addressed in a unified manner, with various tasks being associated with each other through sharing the same paradigm. However, neural networks are black boxes and rely on probability computation. Making mistakes is inevitable. Therefore, estimating the reliability and trustworthiness (in other words, uncertainty) of neural networks becomes a key research direction, which plays a crucial role in reducing models' risks and making better decisions. Therefore, in this survey, we provide a comprehensive review of uncertainty-relevant works in the NLP field. Considering the data and paradigms characteristics, we first categorize the sources of uncertainty in natural language into three types, including input, system, and output. Then, we systemically review uncertainty quantification approaches and the main applications. Finally, we discuss the challenges of uncertainty estimation in NLP and discuss potential future directions, taking into account recent trends in the field. Though there have been a few surveys about uncertainty estimation, our work is the first to review uncertainty from the NLP perspective.

cs.CL↗

arXiv2023

Uncertainty-Aware Unlikelihood Learning Improves Generative Aspect Sentiment Quad Prediction

Recently, aspect sentiment quad prediction has received widespread attention in the field of aspect-based sentiment analysis. Existing studies extract quadruplets via pre-trained generative language models to paraphrase the original sentence into a templated target sequence. However, previous works only focus on what to generate but ignore what not to generate. We argue that considering the negative samples also leads to potential benefits. In this work, we propose a template-agnostic method to control the token-level generation, which boosts original learning and reduces mistakes simultaneously. Specifically, we introduce Monte Carlo dropout to understand the built-in uncertainty of pre-trained language models, acquiring the noises and errors. We further propose marginalized unlikelihood learning to suppress the uncertainty-aware mistake tokens. Finally, we introduce minimization entropy to balance the effects of marginalized unlikelihood learning. Extensive experiments on four public datasets demonstrate the effectiveness of our approach on various generation templates.

cs.CL↗

arXiv2023

DataAI-6G: A System Parameters Configurable Channel Dataset for AI-6G Research

With the acceleration of the commercialization of fifth generation (5G) mobile communication technology and the research for 6G communication systems, the communication system has the characteristics of high frequency, multi-band, high speed movement of users and large antenna array. These bring many difficulties to obtain accurate channel state information (CSI), which makes the performance of traditional communication methods be greatly restricted. Therefore, there has been a lot of interest in using artificial intelligence (AI) instead of traditional methods to improve performance. A common and accurate dataset is essential for the research of AI communication. However, the common datasets nowadays still lack some important features, such as mobile features, spatial non-stationary features etc. To address these issues, we give a dataset for future 6G communication. In this dataset, we address these issues with specific simulation methods and accompanying code processing.

eess.SP↗

arXiv2023

Ground calibration of Gamma-Ray Detectors of GECAM-C

As a new member of GECAM mission, GECAM-C (also named High Energy Burst Searcher, HEBS) was launched onboard the SATech-01 satellite on July 27th, 2022, which is capable to monitor gamma-ray transients from $\sim$ 6 keV to 6 MeV. As the main detector, there are 12 gamma-ray detectors (GRDs) equipped for GECAM-C. In order to verify the GECAM-C GRD detector performance and to validate the Monte Carlo simulations of detector response, comprehensive on-ground calibration experiments have been performed using X-ray beam and radioactive sources, including Energy-Channel relation, energy resolution, detection efficiency, SiPM voltage-gain relation and the non-uniformity of positional response. In this paper, the detailed calibration campaigns and data analysis results for GECAM-C GRDs are presented, demonstrating the excellent performance of GECAM-C GRD detectors.

astro-ph.IM↗

arXiv2023

E-NER: Evidential Deep Learning for Trustworthy Named Entity Recognition

Most named entity recognition (NER) systems focus on improving model performance, ignoring the need to quantify model uncertainty, which is critical to the reliability of NER systems in open environments. Evidential deep learning (EDL) has recently been proposed as a promising solution to explicitly model predictive uncertainty for classification tasks. However, directly applying EDL to NER applications faces two challenges, i.e., the problems of sparse entities and OOV/OOD entities in NER tasks. To address these challenges, we propose a trustworthy NER framework named E-NER by introducing two uncertainty-guided loss terms to the conventional EDL, along with a series of uncertainty-guided training strategies. Experiments show that E-NER can be applied to multiple NER paradigms to obtain accurate uncertainty estimation. Furthermore, compared to state-of-the-art baselines, the proposed method achieves a better OOV/OOD detection performance and better generalization ability on OOV entities.

cs.CL↗

arXiv2023

The precursor of GRB211211A: a tide-induced giant quake?

The equilibrium configuration of a solid strange star in the final inspiral phase with another compact object is generally discussed, and the starquake-related issue is revisited, for a special purpose to understand the precursor emission of binary compact star merger events (e.g., that of GRB211211A). As the binary system inspirals inward due to gravitational wave radiation, the ellipticity of the solid strangeon star increases due to the growing tidal field of its compact companion. Elastic energy is hence accumulated during the inspiral stage which might trigger a starquake before the merger when exceeds a critical value. The energy released during such starquakes is calculated and compared to the precursor observation of GRB211211A. The result shows that the energy might be insufficient for binary strangeon-star case unless the entire solid strangeon star shatters, and hence favors a black hole-strangeon star scenario for GRB211211A. The timescale of the precursor as well as the frequency of the observed quasi-periodic-oscillation have also been discussed in the starquake model.

astro-ph.HE↗