arXiv subjects

Mingzhe Yang

Publications and source records attributed to Mingzhe Yang.

17 recordsLinked to original sources

arXiv2026

SLAI T-Rex: Full-Parameter Post-training of the DeepSeek-V4 Family on Ascend SuperPOD

Full-parameter post-training of trillion-parameter-scale MoE models introduces substantial system-level challenges for large-scale distributed training, including severe memory pressure, non-overlapped communication overhead, and inefficient kernel execution. While most large-scale LLM training systems are built around GPU-based clusters, this report presents an end-to-end optimization practice on the Ascend NPU SuperPOD. Using the DeepSeek-V4 model family as the target workload, we develop a hierarchical optimization framework spanning model-level parallelism, computation-communication orchestration, and low-level kernel execution. The resulting system achieves 34.22% Model FLOPs Utilization (MFU) with a 2.93x improvement over the open-source baseline recipe while maintaining training stability. Building on this optimized infrastructure, we further establish a CPT and SFT workflow for complex Operations Research (OR) tasks. We refer to the integrated framework as SLAI T-Rex. Using DeepSeek-V4-Flash, we develop OR-oriented CPT and SFT data pipelines that combine collected domain resources with solver-verified synthetic optimization documents. The resulting dataset contains 10K high-quality SFT samples spanning four task categories and three problem representations. The specialized model achieves the highest average zero-shot Pass@1 score among the evaluated models, reaching 71.81% and outperforming GPT-5.4-Mini and the base DeepSeek-V4-Flash model by 3.98 and 11.27 percentage points, respectively. Overall, this work demonstrates a full-stack pathway from efficient trillion-parameter model post-training on Ascend infra to domain-specialized Flash models for solver-grounded mathematical modeling, advancing frontier-model systems for complex reasoning.

cs.CL↗

arXiv2026

Tunable high-charge relativistic electron beams via direct laser acceleration in hohlraum-preheated foam targets

Direct laser acceleration (DLA) in near-critical-density (NCD) plasmas can efficiently generate high-charge relativistic electron beams, yet beam parameters depend critically on precise plasma state manipulation. Solid-ablation NCD plasmas evolve rapidly, posing severe controllability challenges. We produce NCD plasma via indirectly heating foam targets with ns laser driven hohlraum soft X-ray. Electrons are generated through irradiating the plasma with another picosecond laser. Tuning the laser pulse delay $τ$ enables control of plasma profiles and beam parameters. Experiments show that when the foam is heated ($τ$ = 6 ns, 9 ns), the beam exhibits $T \sim 13$ MeV effective temperature, $E_k \sim 80$ MeV cutoff energy, and hundreds of nC/sr charge for $E_k > 7.5$ MeV. These values are significantly higher than those from solid-foil ($T$ $\sim$ 2.7 MeV, $E_k$ $\sim$ 20 MeV, $Q$ $\sim$ 9 nC/sr) and cold-foam ($T$ $\sim$ 12 MeV, $E_k$ $\sim$ 50 MeV, $Q$ $\sim$ 5 nC/sr) interactions. At a longer delay of $τ$ = 15 ns, the charge increases further while the temperature decreases, and at a shorter delay of $τ$ = 3 ns, both temperature and charge are lower. 3D PIC simulations link these observations to the interplay between the microstructure of the cold foam and the evolving plasma density profile at different delay times, which together determine the beam charge, effective temperature, and divergence. The finding provides a routine to generate and tailor the relativistic electron beams, which is essential for designing laser-driven electron sources for high energy density physics and photonuclear reaction applications.

physics.plasm-ph↗

arXiv2026

Observation of stopping power reduction at strong ion-plasma coupling

Ion stopping in dense plasma is crucial for stellar evolution and fusion ignition. However, its behavior in the strong ion-plasma coupling regime beyond the linear limit has long remained elusive, due to formidable experimental challenges. Here we report the first experimental investigation of ion stopping at an unprecedented coupling parameter exceeding unity, achieved by sending laser-accelerated short-pulse and intense quasi-monoenergetic carbon ions ($\sim$583 keV/u, C$^{5+}$) into a uniform, long-lived, well-characterized dense plasma target ($T_e$ $\approx$ 17 eV, $n_e$ $\approx$ 4$\times$10$^{20}$ cm$^{-3}$). By simultaneously measuring ion energy loss and charge-state evolution, we eliminated key experimental ambiguities arising from charge-state determination. Our results clearly show a reduction in stopping power compared with predictions from standard linear dielectric response or binary collision models, and they agree well with the hybrid calculation of molecular dynamics with quantum corrections. The importance of nonlinear screening effects arising from many-body interactions and quantum effects due to the wave nature of electrons was demonstrated at strong coupling. This work establishes a definitive high-fidelity experimental benchmark for collisional dynamics in the strong-coupling regime. It offers critical insight for accurate modeling of energy transport in inertial confinement fusion and astrophysical plasmas.

physics.plasm-ph↗

arXiv2026

Partial Effective Information Decomposition for Synergistic Causality

Causality is a central topic in scientific inquiry, yet for complex systems, the identification and analysis of synergistic causation remain a challenging and fundamental problem. In the context of causal relations among multivariate variables, a decomposition framework grounded in interventionist causation is still lacking. To address this gap, this paper proposes Partial Effective Information Decomposition (PEID), a framework that decomposes the influence of multiple source variables on a target variable under maximum-entropy interventions into unique and synergistic information, thereby providing a unified and computable characterization of synergistic causal relations. Theoretically, in the three-variable case, the proposed framework is compatible with the major axioms of Partial Information Decomposition (PID). Empirically, under maximum-entropy interventions, correlations among input variables are removed, causing redundancy to vanish and thereby enabling PEID to compute synergistic relations. Furthermore, based on this framework, it is possible to define causal graphs containing hyperedges as well as downward causation, thus offering a unified toolkit for analyzing cross-scale and multivariate causal mechanisms in complex systems. Finally, applying the framework to a machine-learning-based air quality forecasting task on KnowAir-V2, we demonstrate that PEID can extract interpretable inter-station causal structures from a learned dynamical model. These results suggest that PEID provides a general interventionist information-theoretic tool for analyzing multivariate and synergistic causal mechanisms in complex systems.

stat.ML↗

arXiv2025

SVD-based Causal Emergence for Gaussian Iterative Systems

Causal emergence (CE) based on effective information (EI) demonstrates that macro-states can exhibit stronger causal effects than micro-states in dynamics. However, the identification of CE and the maximization of EI both rely on coarse-graining strategies, which is a key challenge. A recently proposed CE framework based on approximate dynamical reversibility, utilizing singular value decomposition (SVD), is independent of coarse-graining. Still, it is limited to transition probability matrices (TPM) in discrete states. To address this, this article proposes a novel CE quantification framework for Gaussian iterative systems (GIS), based on approximate dynamical reversibility derived from the SVD of inverse covariance matrices in forward and backward dynamics. The positive correlation between SVD-based and EI-based CE, along with the equivalence condition, is given analytically. After that, we provide precise coarse-graining strategies directly from singular value spectra and orthogonal matrices. This new framework can be applied to any dynamical system with continuous states and Gaussian noise, such as auto-regressive growth models, Markov-Gaussian systems, and even SIR modeling using neural networks (NN). Numerical simulations on typical cases validate our theory and offer a new approach to studying the CE phenomenon, emphasizing noise and covariance over dynamical functions in both known models and machine learning.

nlin.CD↗

arXiv2025

StegOT: Trade-offs in Steganography via Optimal Transport

Image hiding is often referred to as steganography, which aims to hide a secret image in a cover image of the same resolution. Many steganography models are based on genera-tive adversarial networks (GANs) and variational autoencoders (VAEs). However, most existing models suffer from mode collapse. Mode collapse will lead to an information imbalance between the cover and secret images in the stego image and further affect the subsequent extraction. To address these challenges, this paper proposes StegOT, an autoencoder-based steganography model incorporating optimal transport theory. We designed the multiple channel optimal transport (MCOT) module to transform the feature distribution, which exhibits multiple peaks, into a single peak to achieve the trade-off of information. Experiments demonstrate that we not only achieve a trade-off between the cover and secret images but also enhance the quality of both the stego and recovery images. The source code will be released on https://github.com/Rss1124/StegOT.

cs.CV↗

arXiv2025

Causal Emergence of Consciousness through Learned Multiscale Neural Dynamics in Mice

Consciousness spans macroscopic experience and microscopic neuronal activity, yet linking these scales remains challenging. Prevailing theories, such as Integrated Information Theory, focus on a single scale, overlooking how causal power and its dynamics unfold across scales. Progress is constrained by scarce cross-scale data and difficulties in quantifying multiscale causality and dynamics. Here, we present a machine learning framework that infers multiscale causal variables and their dynamics from near-cellular-resolution calcium imaging in the mouse dorsal cortex. At lower levels, variables primarily aggregate input-driven information, whereas at higher levels they realize causality through metastable or saddle-point dynamics during wakefulness, collapsing into localized, stochastic dynamics under anesthesia. A one-dimensional top-level conscious variable captures the majority of causal power, yet variables across other scales also contribute substantially, giving rise to high emergent complexity in the conscious state. Together, these findings provide a multiscale causal framework that links neural activity to conscious states.

q-bio.NC↗

arXiv2025

Express Diagnostic of Intense Laser-driven MeV Radiation Source using Copper Isotopes

We explored the generation and diagnosis of high-brightness MeV bremsstrahlung radiation caused by intense beam of relativistic electrons propagating in a tantalum converter. The intense electron beam was produced through direct laser acceleration mechanism in the interaction of relativistic high-power sub-ps laser pulse with near critical density plasma. We propose to detect the divergence angle and photon fluence of high-brightness and high-energy gamma radiation source based on the nuclear activation method. The radioactive 62^Cu was generated through photonuclear reactions 63^Cu(gamma,n) 62^Cu and the subsequent beta^+ decay of 62^Cu was measured to derive characteristics of the gamma radiation source. This method provides an express approach to diagnose the laser-driven MeV radiation source and a potential efficient way to produce 62^Cu isotopes.

physics.acc-ph↗

arXiv2025

Dynamical Reversibility and A New Theory of Causal Emergence based on SVD

The theory of causal emergence (CE) with effective information (EI) posits that complex systems can exhibit CE, where macro-dynamics show stronger causal effects than micro-dynamics. A key challenge of this theory is its dependence on coarse-graining method. In this paper, we introduce a fresh concept of approximate dynamical reversibility and establish a novel framework for CE based on this. By applying singular value decomposition(SVD) to Markov dynamics, we find that the essence of CE lies in the presence of redundancy, represented by the irreversible and correlated information pathways. Therefore, CE can be quantified as the potential maximal efficiency increase for dynamical reversibility or information transmission. We also demonstrate a strong correlation between the approximate dynamical reversibility and EI, establishing an equivalence between the SVD and EI maximization frameworks for quantifying CE, supported by theoretical insights and numerical examples from Boolean networks, cellular automata, and complex networks. Importantly, our SVD-based CE framework is independent of specific coarse-graining techniques and effectively captures the fundamental characteristics of the dynamics.

cond-mat.stat-mech↗

arXiv2025

Do Expressions Change Decisions? Exploring the Impact of AI's Explanation Tone on Decision-Making

Explanatory information helps users to evaluate the suggestions offered by AI-driven decision support systems. With large language models, adjusting explanation expressions has become much easier. However, how these expressions influence human decision-making remains largely unexplored. This study investigated the effect of explanation tone (e.g., formal or humorous) on decision-making, focusing on AI roles and user attributes. We conducted user experiments across three scenarios depending on AI roles (assistant, second-opinion provider, and expert) using datasets designed with varying tones. The results revealed that tone significantly influenced decision-making regardless of user attributes in the second-opinion scenario, whereas its impact varied by user attributes in the assistant and expert scenarios. In addition, older users were more influenced by tone, and highly extroverted users exhibited discrepancies between their perceptions and decisions. Furthermore, open-ended questionnaires highlighted that users expect tone adjustments to enhance their experience while emphasizing the importance of tone consistency and ethical considerations. Our findings provide crucial insights into the design of explanation expressions.

cs.HC↗

arXiv2025

Quantifying system-environment synergistic information by effective information decomposition

What is the most crucial characteristic of a system with life activity? Currently, many theories have attempted to explain the most essential difference between living systems and general systems, such as the self-organization theory and the free energy principle, but there is a lack of a reasonable indicator that can measure to what extent a system can be regarded as a system with life characteristics, especially the lack of attention to the dynamic characteristics of life systems. In this article, we propose a new indicator at the level of dynamic mechanisms to measure the ability of a system to flexibly respond to the environment. We proved that this indicator satisfies the axiom system of multivariate information decomposition in the partial information decomposition (PID) framework. Through further disassembly and analysis of this indicator, we found that it is determined by the degree of entanglement between system and environmental variables in the dynamics and the magnitude of noise. We conducted measurements on cellular automata (CA), random Boolean networks, and real gene regulatory networks (GRN), verified its relationship with the type of CA and the Langton parameter, and identified that the feedback loops have high abilities to flexibly respond to the environment on the GRN. We also combined machine learning technology to prove that this framework can be applied in the case of unknown dynamics.

q-bio.MN↗

arXiv2024

System Information Decomposition

To characterize the complex higher-order interactions among variables within a system, this study introduces a novel framework, termed System Information Decomposition (SID), aimed at decomposing the information entropy of variables into information atoms based on their interrelations. Diverging from the established Partial Information Decomposition (PID) framework, which predominantly concentrates on the directional interactions stemming from an array of source variables to a single target variable, SID adopts a holistic approach, scrutinizing the interactions across all variables within the system. Specifically, we proved all the information atoms are symmetric, which means the disentanglement of unique, redundant, and synergistic information from any specific target variable. Hence, our proposed SID framework can capture the symmetric pairwise and higher-order relationships among variables. This advance positions SID as a promising framework with the potential to foster a deeper understanding of higher-order relationships within complex systems across disciplines.

cs.IT↗

arXiv2024

SwipeGANSpace: Swipe-to-Compare Image Generation via Efficient Latent Space Exploration

Generating preferred images using generative adversarial networks (GANs) is challenging owing to the high-dimensional nature of latent space. In this study, we propose a novel approach that uses simple user-swipe interactions to generate preferred images for users. To effectively explore the latent space with only swipe interactions, we apply principal component analysis to the latent space of the StyleGAN, creating meaningful subspaces. We use a multi-armed bandit algorithm to decide the dimensions to explore, focusing on the preferences of the user. Experiments show that our method is more efficient in generating preferred images than the baseline methods. Furthermore, changes in preferred images during image generation or the display of entirely different image styles were observed to provide new inspirations, subsequently altering user preferences. This highlights the dynamic nature of user preferences, which our proposed approach recognizes and enhances.

cs.HC↗

arXiv2024

Fair Machine Guidance to Enhance Fair Decision Making in Biased People

Teaching unbiased decision-making is crucial for addressing biased decision-making in daily life. Although both raising awareness of personal biases and providing guidance on unbiased decision-making are essential, the latter topics remains under-researched. In this study, we developed and evaluated an AI system aimed at educating individuals on making unbiased decisions using fairness-aware machine learning. In a between-subjects experimental design, 99 participants who were prone to bias performed personal assessment tasks. They were divided into two groups: a) those who received AI guidance for fair decision-making before the task and b) those who received no such guidance but were informed of their biases. The results suggest that although several participants doubted the fairness of the AI system, fair machine guidance prompted them to reassess their views regarding fairness, reflect on their biases, and modify their decision-making criteria. Our findings provide insights into the design of AI systems for guiding fair decision-making in humans.

cs.HC↗

arXiv2024

Emergence and Causality in Complex Systems: A Survey on Causal Emergence and Related Quantitative Studies

Emergence and causality are two fundamental concepts for understanding complex systems. They are interconnected. On one hand, emergence refers to the phenomenon where macroscopic properties cannot be solely attributed to the cause of individual properties. On the other hand, causality can exhibit emergence, meaning that new causal laws may arise as we increase the level of abstraction. Causal emergence theory aims to bridge these two concepts and even employs measures of causality to quantify emergence. This paper provides a comprehensive review of recent advancements in quantitative theories and applications of causal emergence. Two key problems are addressed: quantifying causal emergence and identifying it in data. Addressing the latter requires the use of machine learning techniques, thus establishing a connection between causal emergence and artificial intelligence. We highlighted that the architectures used for identifying causal emergence are shared by causal representation learning, causal model abstraction, and world model-based reinforcement learning. Consequently, progress in any of these areas can benefit the others. Potential applications and future perspectives are also discussed in the final section of the review.

physics.soc-ph↗

arXiv2023

Finding emergence in data by maximizing effective information

Quantifying emergence and modeling emergent dynamics in a data-driven manner for complex dynamical systems is challenging due to the lack of direct observations at the micro-level. Thus, it's crucial to develop a framework to identify emergent phenomena and capture emergent dynamics at the macro-level using available data. Inspired by the theory of causal emergence (CE), this paper introduces a machine learning framework to learn macro-dynamics in an emergent latent space and quantify the degree of CE. The framework maximizes effective information, resulting in a macro-dynamics model with enhanced causal effects. Experimental results on simulated and real data demonstrate the effectiveness of the proposed framework. It quantifies degrees of CE effectively under various conditions and reveals distinct influences of different noise types. It can learn a one-dimensional coarse-grained macro-state from fMRI data, to represent complex neural activities during movie clip viewing. Furthermore, improved generalization to different test environments is observed across all simulation data.

physics.soc-ph↗

arXiv2020

Iterative Machine Teaching without Teachers

Iterative machine teaching is a method for selecting an optimal teaching example that enables a student to efficiently learn a target concept at each iteration. Existing studies on iterative machine teaching are based on supervised machine learning and assume that there are teachers who know the true answers of all teaching examples. In this study, we consider an unsupervised case where such teachers do not exist; that is, we cannot access the true answer of any teaching example. Students are given a teaching example at each iteration, but there is no guarantee if the corresponding label is correct. Recent studies on crowdsourcing have developed methods for estimating the true answers from crowdsourcing responses. In this study, we apply these to iterative machine teaching for estimating the true labels of teaching examples along with student models that are used for teaching. Our method supports the collaborative learning of students without teachers. The experimental results show that the teaching performance of our method is particularly effective for low-level students in particular.

cs.LG↗