arXiv subjects

Zhen Zhang

Publications and source records attributed to Zhen Zhang.

At least 289 records · Page 16Linked to original sources

arXiv2024

Effects of incompressibility on the neutron-proton equilibration in $^{70}$Zn + $^{70}$Zn collisions at 35 MeV/nucleon

Background: The primary goal of studying isospin dynamics via heavy-ion reactions is to explore the isospin dependence of effective interactions within the nuclear equation of state (EOS). Purpose: This work aims to investigate the effects of nuclear incompressibility ($ K_0 $) on neutron-proton equilibration in projectile-like fragments (PLFs). Method: We simulate $^{70}$Zn + $^{70}$Zn collisions at 35 MeV/nucleon using the isospin-dependent quantum molecular dynamics (IQMD) model, coupled with the statistical decay code GEMINI. Results: The IQMD simulations not only reproduce experimental data patterns but also reveal the dynamic mechanisms underlying the binary breakup of PLFs. The rotation of PLFs is influenced by the transformation of angular momentum, which is connected to the isoscalar component of the EOS. This connection explains why shifts in $ K_0 $ affect the description of neutron-proton equilibration as measured by PLF rotation. The simulations demonstrate that a model with a smaller $ K_0 $ paired with a softer symmetry energy, or a larger $ K_0 $ with a slightly stiffer symmetry energy, both offer better indications of neutron-proton equilibration. Conclusion: Considering the uncertainty in $ K_0 $, the slope of the symmetry energy is constrained within the range of $ L = 20 \sim 40 $ MeV, providing valuable insights into the nuclear equation of state.

nucl-th↗

arXiv2024

Robust Constraints on the Physics of the MeV Emission Line in GRB 221009A from Optical Depth Arguments

The brightest-of-all-time gamma-ray burst (GRB), GRB 221009A, is the first GRB observed to have emission line (up to 37 MeV) in its prompt emission spectra. It is naturally explained as \pair annihilation line that was Doppler boosted in the relativistic jet of the GRB. In this work, we repeatedly apply the simple optical depth argument to different physical processes necessary to produce an observable \pair annihilation line. This approach results in robust constraints on the physics of the line: We conclude that in GRB 221009A, the \pair pairs were produced at a radius greater than $4.3\times 10^{15}$\,cm from the central engine, and annihilated in a region between $1.4\times 10^{16}$\,cm and $4.3\times 10^{16}$\,cm. From these constraints, we established a self-consistent picture of \pair production, cooling, and annihilation. We also derived a criterion for pair production in the GRB prompt emission: $E_{\rm{iso}} \gtrsim3.3\times 10^{53} E_{\rm{peak},100} (1+z) R^2_{\rm{prod},16}~\text{erg}$. Using this criterion, we find tens of candidate GRBs that could have produced \pair in prompt emissions to annihilate. GRB 221009A is with the highest likelihood according to this criterion. We also predict the presence of a thermal radiation, with a time-evolving black body temperature, sweeping through soft X-ray during the prompt emission phase.

astro-ph.HE↗

arXiv2024

ChannelGPT: A Large Model to Generate Digital Twin Channel for 6G Environment Intelligence

6G is envisaged to provide multimodal sensing, pervasive intelligence, global coverage, global coverage, etc., which poses extreme intricacy and new challenges to the network design and optimization. As the core part of 6G, wireless channel is the carrier and enabler for the flourishing technologies and novel services, which intrinsically determines the ultimate system performance. However, how to describe and utilize the complicated and high-dynamic characteristics of wireless channel accurately and effectively still remains great hallenges. To tackle this, digital twin is envisioned as a powerful technology to migrate the physical entities to virtual and computational world. In this article, we propose a large model driven digital twin channel generator (ChannelGPT) embedded with environment intelligence (EI) to enable pervasive intelligence paradigm for 6G network. EI is an iterative and interactive procedure to boost the system performance with online environment adaptivity. Firstly, ChannelGPT is capable of utilization the multimodal data from wireless channel and corresponding physical environment with the equipped sensing ability. Then, based on the fine-tuned large model, ChannelGPT can generate multi-scenario channel parameters, associated map information and wireless knowledge simultaneously, in terms of each task requirement. Furthermore, with the support of online multidimensional channel and environment information, the network entity will make accurate and immediate decisions for each 6G system layer. In practice, we also establish a ChannelGPT prototype to generate high-fidelity channel data for varied scenarios to validate the accuracy and generalization ability based on environment intelligence.

eess.SP↗

arXiv2024

Globally Rigid Convex Braced Polygons

Here we propose a class of frameworks in the plane, braced polygons, that may be globally rigid and are analogous to convex polyopes in 3 space that are rigid by Cauchy's rigidity Theorem in 1813.

math.MG↗

arXiv2024

Towards Dynamic Graph Neural Networks with Provably High-Order Expressive Power

Dynamic Graph Neural Networks (DyGNNs) have garnered increasing research attention for learning representations on evolving graphs. Despite their effectiveness, the limited expressive power of existing DyGNNs hinders them from capturing important evolving patterns of dynamic graphs. Although some works attempt to enhance expressive capability with heuristic features, there remains a lack of DyGNN frameworks with provable and quantifiable high-order expressive power. To address this research gap, we firstly propose the k-dimensional Dynamic WL tests (k-DWL) as the referencing algorithms to quantify the expressive power of DyGNNs. We demonstrate that the expressive power of existing DyGNNs is upper bounded by the 1-DWL test. To enhance the expressive power, we propose Dynamic Graph Neural Network with High-order expressive power (HopeDGN), which updates the representation of central node pair by aggregating the interaction history with neighboring node pairs. Our theoretical results demonstrate that HopeDGN can achieve expressive power equivalent to the 2-DWL test. We then present a Transformer-based implementation for the local variant of HopeDGN. Experimental results show that HopeDGN achieved performance improvements of up to 3.12%, demonstrating the effectiveness of HopeDGN.

cs.LG↗

arXiv2024

Wireless Environment Information Sensing, Feature, Semantic, and Knowledge: Four Steps Towards 6G AI-Enabled Air Interface

The air interface technology plays a crucial role in optimizing the communication quality for users. To address the challenges brought by the radio channel variations to air interface design, this article proposes a framework of wireless environment information-aided 6G AI-enabled air interface (WEI-6G AI$^{2}$), which actively acquires real-time environment details to facilitate channel fading prediction and communication technology optimization. Specifically, we first outline the role of WEI in supporting the 6G AI$^{2}$ in scenario adaptability, real-time inference, and proactive action. Then, WEI is delineated into four progressive steps: raw sensing data, features obtained by data dimensionality reduction, semantics tailored to tasks, and knowledge that quantifies the environmental impact on the channel. To validate the availability and compare the effect of different types of WEI, a path loss prediction use case is designed. The results demonstrate that leveraging environment knowledge requires only 2.2 ms of model inference time, which can effectively support real-time design for future 6G AI$^{2}$. Additionally, WEI can reduce the pilot overhead by 25\%. Finally, several open issues are pointed out, including multi-modal sensing data synchronization and information extraction method construction.

eess.SP↗

arXiv2024

Computational electron-phonon superconductivity: from theoretical physics to material science

The search for room-temperature superconductors is a major challenge in modern physics. The discovery of copper-oxide superconductors in 1986 brought hope but also revealed complex mechanisms that are difficult to analyze and compute. In contrast, the traditional electron-phonon coupling (EPC) mechanism facilitated the practical realization of superconductivity in metallic hydrogen. Since 2015, the discovery of new hydrogen compounds has shown that EPC can enable room-temperature superconductivity under high pressures, driving extensive research. Advances in computational capabilities, especially exascale computing, now allow for the exploration of millions of materials. This paper reviews newly predicted superconducting systems in 2023-2024, focusing on hydrides, boron-carbon systems, and compounds with nitrogen, carbon, and pure metals. Although many computationally predicted high-Tc superconductors were not experimentally confirmed, some low-temperature superconductors were successfully synthesized. This paper provides a review of these developments and future research directions.

cond-mat.mtrl-sci↗

arXiv2024

PipeFill: Using GPUs During Bubbles in Pipeline-parallel LLM Training

Training Deep Neural Networks (DNNs) with billions of parameters generally involves pipeline-parallel (PP) execution. Unfortunately, PP model training can use GPUs inefficiently, especially at large scale, due to idle GPU time caused by pipeline bubbles, which are often 15-30% and can exceed 60% of the training job's GPU allocation. To improve the GPU utilization of PP model training, this paper describes PipeFill, which fills pipeline bubbles with execution of other pending jobs. By leveraging bubble GPU time, PipeFill reduces the GPU utilization sacrifice associated with scaling-up of large-model training. To context-switch between fill jobs and the main training job with minimal overhead to the main job, and maximize fill job efficiency, PipeFill carefully fits fill job work to measured bubble durations and GPU memory availability, introduces explicit pipeline-bubble instructions, and orchestrates placement and execution of fill jobs in pipeline bubbles. Experiments show that PipeFill can increase overall utilization by up to 63% for GPUs used in large-scale LLM training, with <2% slowdown of the training job, and 5-15% even for low-scale LLM training. For large-scale LLM training on 8K GPUs, the 63% increase translates to up to 2.6K additional GPUs worth of work completed.

cs.DC↗

arXiv2024

Implication of Jet Physics from MeV Line Emission of GRB 221009A

Ultrarelativistic jets are believed to play an important role in producing prompt emission and afterglow of gamma-ray bursts (GRBs), but the nature of the jet is poorly known owing to the lack of decisive features observed in the prompt emission. The discovery of an emission line evolving from about 37 to 6 MeV in the brightest-of-all-time GRB 221009A provides an unprecedented opportunity to probe GRB jet physics. The time evolution of the central energy of the line with power-law index $-1$ is naturally explained by the high-latitude curvature effect. Under the assumption that the line emission is generated in the prompt emission by $e^\pm$ pair production, cooling, and annihilation in the jet, we can strictly constrain jet physics with observed line emission properties. We find that the radius of the emission region is $r\gtrsim10^{16}$ cm. The narrow line width of $\sim10\%$ requires that the line emission occurs within $\sim10\%$ of the dynamical time, which further implies short timescales of pair cooling to the nonrelativistic state and pair annihilation, as well as a slightly clumpy emission region. If the jet's Lorentz factor is $Γ\gtrsim400$, the fast cooling requirement needs an energy density of magnetic field in the jet much larger than that of prompt gamma rays, i.e., a magnetically dominated jet. The temporal behavior of line flux suggests some angle dependence of line emission. We also discuss the difficulties of other scenarios for the observed emission line.

astro-ph.HE↗

arXiv2024

Numerical Investigations on Dilute Cold Plasma Potential and Electron Temperature

Simulation results are presented to demonstrate electron temperature and electrical potential development in dilute and cold plasma development. The simulation method is a hybrid method which adopted fluid model for electrons due to their high mobility, while heavy ions and neutrals are modelled with the direct simulation Monte Carlo and Particle-In-Cell methods. The flows include steady, starting-up and shutting-down scenarios. The goal is to illustrate the exponential behaviors which were predicted in several recently developed formulas. Those formulas include many coefficients related with local properties, and they are difficult to determine. Hence, those trends can only efficiently demonstrate by numerical simulations which are more convenient than experimental measurements. The results confirm several facts. For steady plasma flows, the electron temperature and potential profiles are smooth, very likely, they can be approximated with exponential functions. For unsteady flows, the property developing trends in the shutting down or starting-up processes change monotonically. Further, at locations with large gradients, the property change trends are less ideal than those formulas. This is consistent with the assumptions with which those formulas were developed.

physics.plasm-ph↗

arXiv2024

An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting

This paper introduces a novel incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting (MCS) problems, enabling decision makers to progressively provide assignment example preference information. Specifically, we first construct a max-margin optimization-based model to model potentially non-monotonic preferences and inconsistent assignment example preference information in each iteration of the incremental preference elicitation process. Using the optimal objective function value of the max-margin optimization-based model, we devise information amount measurement methods and question selection strategies to pinpoint the most informative alternative in each iteration within the framework of uncertainty sampling in active learning. Once the termination criterion is satisfied, the sorting result for non-reference alternatives can be determined through the use of two optimization models, i.e., the max-margin optimization-based model and the complexity controlling optimization model. Subsequently, two incremental preference elicitation-based algorithms are developed to learn potentially non-monotonic preferences, considering different termination criteria. Ultimately, we apply the proposed approach to a credit rating problem to elucidate the detailed implementation steps, and perform computational experiments on both artificial and real-world data sets to compare the proposed question selection strategies with several benchmark strategies.

cs.AI↗

arXiv2024

Lexicographic optimization-based approaches to learning a representative model for multi-criteria sorting with non-monotonic criteria

Deriving a representative model using value function-based methods from the perspective of preference disaggregation has emerged as a prominent and growing topic in multi-criteria sorting (MCS) problems. A noteworthy observation is that many existing approaches to learning a representative model for MCS problems traditionally assume the monotonicity of criteria, which may not always align with the complexities found in real-world MCS scenarios. Consequently, this paper proposes some approaches to learning a representative model for MCS problems with non-monotonic criteria through the integration of the threshold-based value-driven sorting procedure. To do so, we first define some transformation functions to map the marginal values and category thresholds into a UTA-like functional space. Subsequently, we construct constraint sets to model non-monotonic criteria in MCS problems and develop optimization models to check and rectify the inconsistency of the decision maker's assignment example preference information. By simultaneously considering the complexity and discriminative power of the models, two distinct lexicographic optimization-based approaches are developed to derive a representative model for MCS problems with non-monotonic criteria. Eventually, we offer an illustrative example and conduct comprehensive simulation experiments to elaborate the feasibility and validity of the proposed approaches.

cs.AI↗

arXiv2024

InvariantStock: Learning Invariant Features for Mastering the Shifting Market

Accurately predicting stock returns is crucial for effective portfolio management. However, existing methods often overlook a fundamental issue in the market, namely, distribution shifts, making them less practical for predicting future markets or newly listed stocks. This study introduces a novel approach to address this challenge by focusing on the acquisition of invariant features across various environments, thereby enhancing robustness against distribution shifts. Specifically, we present InvariantStock, a designed learning framework comprising two key modules: an environment-aware prediction module and an environment-agnostic module. Through the designed learning of these two modules, the proposed method can learn invariant features across different environments in a straightforward manner, significantly improving its ability to handle distribution shifts in diverse market settings. Our results demonstrate that the proposed InvariantStock not only delivers robust and accurate predictions but also outperforms existing baseline methods in both prediction tasks and backtesting within the dynamically changing markets of China and the United States.

cs.CE↗

arXiv2024

SympGNNs: Symplectic Graph Neural Networks for identifiying high-dimensional Hamiltonian systems and node classification

Existing neural network models to learn Hamiltonian systems, such as SympNets, although accurate in low-dimensions, struggle to learn the correct dynamics for high-dimensional many-body systems. Herein, we introduce Symplectic Graph Neural Networks (SympGNNs) that can effectively handle system identification in high-dimensional Hamiltonian systems, as well as node classification. SympGNNs combines symplectic maps with permutation equivariance, a property of graph neural networks. Specifically, we propose two variants of SympGNNs: i) G-SympGNN and ii) LA-SympGNN, arising from different parameterizations of the kinetic and potential energy. We demonstrate the capabilities of SympGNN on two physical examples: a 40-particle coupled Harmonic oscillator, and a 2000-particle molecular dynamics simulation in a two-dimensional Lennard-Jones potential. Furthermore, we demonstrate the performance of SympGNN in the node classification task, achieving accuracy comparable to the state-of-the-art. We also empirically show that SympGNN can overcome the oversmoothing and heterophily problems, two key challenges in the field of graph neural networks.

cs.LG↗

arXiv2024

Rethinking State Disentanglement in Causal Reinforcement Learning

One of the significant challenges in reinforcement learning (RL) when dealing with noise is estimating latent states from observations. Causality provides rigorous theoretical support for ensuring that the underlying states can be uniquely recovered through identifiability. Consequently, some existing work focuses on establishing identifiability from a causal perspective to aid in the design of algorithms. However, these results are often derived from a purely causal viewpoint, which may overlook the specific RL context. We revisit this research line and find that incorporating RL-specific context can reduce unnecessary assumptions in previous identifiability analyses for latent states. More importantly, removing these assumptions allows algorithm design to go beyond the earlier boundaries constrained by them. Leveraging these insights, we propose a novel approach for general partially observable Markov Decision Processes (POMDPs) by replacing the complicated structural constraints in previous methods with two simple constraints for transition and reward preservation. With the two constraints, the proposed algorithm is guaranteed to disentangle state and noise that is faithful to the underlying dynamics. Empirical evidence from extensive benchmark control tasks demonstrates the superiority of our approach over existing counterparts in effectively disentangling state belief from noise.

cs.LG↗

arXiv2024

Joint Auction in the Online Advertising Market

Online advertising is a primary source of income for e-commerce platforms. In the current advertising pattern, the oriented targets are the online store owners who are willing to pay extra fees to enhance the position of their stores. On the other hand, brand suppliers are also desirable to advertise their products in stores to boost brand sales. However, the currently used advertising mode cannot satisfy the demand of both stores and brand suppliers simultaneously. To address this, we innovatively propose a joint advertising model termed Joint Auction, allowing brand suppliers and stores to collaboratively bid for advertising slots, catering to both their needs. However, conventional advertising auction mechanisms are not suitable for this novel scenario. In this paper, we propose JRegNet, a neural network architecture for the optimal joint auction design, to generate mechanisms that can achieve the optimal revenue and guarantee near dominant strategy incentive compatibility and individual rationality. Finally, multiple experiments are conducted on synthetic and real data to demonstrate that our proposed joint auction significantly improves platform revenue compared to the known baselines.

cs.GT↗

arXiv2024

Relation between the keV-MeV and TeV emission of GRB 221009A and its implications

Gamma-ray bursts (GRBs) are believed to launch relativistic jets, which generate prompt emission by internal processes, and produce long-lasting afterglows by driving external shocks into surrounding medium. However, how the jet powers the external shock is poorly known. The unprecedented observations of the keV-MeV emission with GECAM and the TeV emission with LHAASO of the brightest-of-all-time GRB 221009A offer a great opportunity to study the prompt-to-afterglow transition and the impact of jet on the early dynamics of external shock. In this letter, we find that the cumulative light curve of keV-MeV emission could well fit the rising stage of the TeV light curve of GRB 221009A, with a time delay, $4.45^{+0.26}_{-0.26}$\,s, of TeV emission. Moreover, both the rapid increase in the initial stage and the excess from about \T+260\,s to 270\,s in the TeV light curve are tracking the light-curve bumps in the prompt keV-MeV emission. The close relation between the keV-MeV and TeV emission reveals the continuous energy-injection into the external shock. Assuming an energy-injection rate exactly following the keV-MeV flux of GRB 221009A, including the very early precursor, we build a continuous energy-injection model where the jet Lorentz factor is derived from the TeV time delay, and the TeV data is well fitted, with the TeV excesses interpreted by inverse Compton (IC) scatterings of the inner-coming prompt emission by the energetic electrons in external shock.

astro-ph.HE↗

arXiv2024

Can Wireless Environmental Information Decrease Pilot Overhead: A CSI Prediction Example

Channel state information (CSI) is crucial for massive multi-input multi-output (MIMO) system. As the antenna scale increases, acquiring CSI results in significantly higher system overhead. In this letter, we propose a novel channel prediction method which utilizes wireless environmental information with pilot pattern optimization for CSI prediction (WEI-CSIP). Specifically, scatterers around the mobile station (MS) are abstracted from environmental information using multiview images. Then, an environmental feature map is extracted by a convolutional neural network (CNN). Additionally, the deep probabilistic subsampling (DPS) network acquires an optimal fixed pilot pattern. Finally, a CNN-based channel prediction network is designed to predict the complete CSI, using the environmental feature map and partial CSI. Simulation results show that the WEI-CSIP can reduce pilot overhead from 1/5 to 1/8, while improving prediction accuracy with normalized mean squared error reduced to 0.0113, an improvement of 83.2% compared to traditional channel prediction methods.

eess.SP↗