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Ming Li

Publications and source records attributed to Ming Li.

At least 73 records · Page 4Linked to original sources

First Demonstration of Flip DRAM from Process, Architecture to System to Push DRAM Scaling beyond 4F2: 2F2 Self-aligned Flip Vertical Channel Transistor (FVCT) DRAM and Flip WL (FWL) 3D-DRAM

For the first time, we proposed a novel stacking technology for DRAM scaling by flipping and backside processes, making full use of DRAM wafer's backside and investigating it on both 4F2 and 3D-DRAM. For 4F2 VCT, 2F2 Flip VCT featuring self-aligned back-to-back stacked 1T1C bitcell, with various BL and WL configurations, were studied and key process modules such as self-aligned stacked vertical channel, BL and WL formations, wafer bonding and flipping, substrate thinning and low-R Co storage node (SN) were successfully developed, addressing the potential thermal, misalign and parasitic concerns in the Flip VCT process. A full DRAM DTCO framework was also established from device to mat and chip level. Compared to 4F2 VCT DRAM with the same mat size, 2F2 FVCT delivers 27.5% less parasitics, 11% better sense margin, 16.3% higher charge sharing (CS) speed and 50% less area. For 3D-DRAM, a brand-new flip WL staircase design with peripheral circuit innovations was studied and proved to have 25% density gain, 15.1% faster turn-on speed and 6.8% less CS time, proving further extendibility of flip technology on DRAM.

cond-mat.mes-hall↗

Time Series Analysis in Frequency Domain: A Survey of Open Challenges, Opportunities and Benchmarks

Frequency-domain analysis has emerged as a powerful paradigm for time series analysis, offering unique advantages over traditional time-domain approaches while introducing new theoretical and practical challenges. This survey provides a comprehensive examination of spectral methods from classical Fourier analysis to modern neural operators, systematically summarizing three open challenges in current research: (1) causal structure preservation during spectral transformations, (2) uncertainty quantification in learned frequency representations, and (3) topology-aware analysis for non-Euclidean data structures. Through rigorous reviewing of over 100 studies, we develop a unified taxonomy that bridges conventional spectral techniques with cutting-edge machine learning approaches, while establishing standardized benchmarks for performance evaluation. Our work identifies key knowledge gaps in the field, particularly in geometric deep learning and quantum-enhanced spectral analysis. The survey offers practitioners a systematic framework for method selection and implementation, while charting promising directions for future research in this rapidly evolving domain.

cs.CE↗

From Bench-to-Bedside: A Review of Clinical Trials in Drug Discovery and Development

Clinical trials bridge basic research and clinical application, serving as essential steps in drug development. This review examines clinical trial phases (Phase I [safety assessment], Phase II [efficacy evaluation], Phase III [large-scale validation], and Phase IV [post-marketing surveillance]), highlighting the distinct characteristics and interconnections. Major challenges are identified, including ethical compliance, participant recruitment, and ensuring diversity and representativeness in trial populations, while proposing evidence-based mitigation strategies. To address these challenges, innovative technologies, such as artificial intelligence, big data analytics, and digital health tools, are transforming trial design and implementation, enhancing efficiency and data quality. Looking forward, the review explores how emerging therapies, including gene therapy and immunotherapy, are reshaping trial design requirements and emphasizes the growing importance of regulatory harmonization and global collaboration. Clinical trials remain central to advancing innovative drug development and improving patient outcomes.

cs.CY↗

Dynamic Multi-Agent Pickup and Delivery in Robotic Cellular Warehousing Systems

Robotic cellular warehousing systems (RCWS) give rise to multi-agent pickup and delivery (MAPD) processes in which robots sequentially collect multiple stock-keeping units (SKUs) for each order. Unlike classical MAPD formulations that assume static tasks, real warehouse operations often involve dynamic order evolution, where new SKUs may be appended to an order while it is being executed. Motivated by this practical requirement, this letter formulates the Dynamic-MAPD problem considering internal order evolution for the first time. Building on the token passing (TP) mechanism, we propose two event-triggered online replanning algorithms. The two strategies target different robot-resource configurations, depending on whether additional robotic resources are available for cooperative assistance. The first, Dynamic-TP, enables an event-triggered dynamic response by allowing robots to replan from their current execution states through priority-aware token acquisition after order updates. The second, Cooperative-TP, further enables reserved robots to assist newly added SKUs while preserving the original order ownership. Simulation results demonstrate that the proposed methods significantly reduce order flowtime compared with static and non-cooperative baselines, thereby improving the order fulfillment efficiency in RCWS.

cs.RO↗

Auto-RecSys: Harnessing Autonomous Research Agents for Industry-Scale Recommender System

Auto-research agents have shown the potential to automate hypothesis generation, experiment execution, and iterative refinement. However, scaling this paradigm to industry-scale recommendation models introduces two challenges: (1) long feedback loops, where model training can take days, making serial iteration prohibitively slow and requiring parallel exploration across multiple research directions; and (2) system complexity, where large configurations, fragile infrastructure dependencies, and multi-day GPU jobs require robust and recoverable execution. We present Auto-RecSys, an autonomous research system for long-horizon experimentation on industry-scale recommendation models. Auto-RecSys addresses these challenges through three harness designs: (1) distributed asynchronous execution for running multiple experiments in parallel across servers, (2) centralized cross-server memory for persistent and recoverable execution across sessions and failures, and (3) cognitive-procedural separation, where natural-language skill files guide LLM reasoning while deterministic scripts enforce operational correctness. Auto-RecSys further employs a dual-loop self-evolving architecture: an Execution Evolution Loop in which model-specific playbooks accumulate operational knowledge by recording failed attempts and crystallizing successful pipelines, and an Idea Evolution Loop in which experimental outcomes inform subsequent ideation. Evaluated on recommendation models, Auto-RecSys significantly reduces the human time required per experiment cycle and improves execution reliability as its playbooks mature.

cs.CL↗

Unveiling the Scaling Potential of Drain Merge through Active (DMtA) in CFETs: Breaking the Super-Via Bottlenecks and Unlocking New PPA Boosters

Drain merge (DM), a super via vertically connecting the common S/D terminals of stacked n/pFETs in Complementary FETs (CFETs), blocks further parasitic optimization and cell scaling. For the first time, this work systematically investigates the state-of-the-art Drain Merge through Active (DMtA), a revolutionary technology reported recently with the DM embedded in the active region, through a comprehensive DTCO framework spanning process integration, contact-configuration-dependent (CTCD) compact modeling, standardcell design, RO evaluation and block-level PPA benchmark on a 32-bit RISC-V Ibex core. By reducing DM parasitics and enabling DM-width optimization, DMtA improves RO frequency by 11.7% over its conventional Drain Merge through field (DMtF) counterpart. Active widening and Area Borrowing, the latter first reported in [8] and exploiting spatial slack in adjacent cells to further enlarge the nanosheet width (WNS), increase the maximum Ibex-core frequency by up to 34.8%. More importantly, DMtA also enables the once GAA-exclusive Hyper-cells on CFETs by merging the active regions across adjacent cell rows, providing a further 8.7% frequency gain. A post-routing floating-output-pin-aware optimization further removes redundant S/D contacts (CTs) and reduces power by 5.3%. Finally, DMtA facilitates more area-efficient 2.5T cell scaling by preserving single-row cell compatibility, reducing post-PR core area by 25.7%.

cond-mat.mes-hall↗

Experimental realization of quantum-computing-enhanced sensing

Quantum algorithms offer computational advantages, yet incorporating them into quantum sensing and converting these advantages into enhanced information acquisition remains challenging. Here, we realize quantum-computing-enhanced sensing in a spin-oscillator architecture, where a microwave cavity provides a high-dimensional quantum register and a coupled superconducting qubit serves as the sensor. The unknown signal directly generates the phase oracle operation, establishing a natural physical interface between quantum computation and sensing. We experimentally demonstrate the first Grover search in a bosonic mode and observe quantum amplitude amplification in a Hilbert space spanning more than 128 photons. For the same number of sensing iterations, our protocol extracts more information and resolves more frequency candidates than classical sequential search. Our results establish signal-driven oracles as a route for harnessing oracle-based quantum algorithms and realizing quantum-computing-enhanced sensing.

quant-ph↗

The Emerging AI Paper-Review Arms Race: Adversarial Co-Evolution in Scholarly Publishing

Generative and agentic AI are reshaping both the production and evaluation of scientific research. These developments are often studied separately, as questions of how AI can produce research and how AI can review it. We argue that this separation misses an increasingly important feature of scholarly publishing: changes on one side alter the incentives, constraints, and behavior of the other. We synthesize 230 scholarly publications and institutional records using a taxonomy of six connected dynamics: production scaling, evaluation automation, evaluation manipulation, defense mechanisms and policy responses, evasion and side effects, and long-horizon ecosystem feedback. The literature shows an emerging progression in which cheaper and faster research production increases pressure on evaluation, AI-mediated evaluation becomes more scalable and repeatable, participants can exploit evaluator regularities, and institutions respond with technical safeguards and policy controls. These responses can in turn induce evasion, redistribute errors and workload, and shape the scholarly records reused by future research and evaluation systems. Evidence is strongest for production and evaluation at scale, reproducible manipulation, and institutional response, while post-policy adaptation and artifact-level long-horizon feedback remain less directly observed. This systems view shifts attention from isolated AI capabilities toward how scholarly actors and AI systems adapt to one another over time.

cs.AI↗

X-SG$^2$S: Safe and Generalizable Gaussian Splatting with X-dimensional Watermarks

3D Gaussian Splatting (3DGS) has been widely used in 3D reconstruction and 3D generation. However, the rapid adoption of 3D Gaussian Splatting raises growing concerns about information leakage and unauthorized use, urging the exploration of effective watermarking techniques. However, existing methods are limited by low capacity, fragility under geometric perturbations, and the infeasible requirement for costly fine-tuning or pipeline modifications, motivating the need for a generalizable, feed-forward framework capable of robust multi-modal embedding with minimal intrusion. In this paper, we propose a new framework X-SG$^2$S which can simultaneously inject 1D to 3D watermarks for copyright protection, while keeping the high fidelity of original 3DGS scenes. Specifically, we first split the watermarks into message patches. A self-adaptive gate is developed to select the injection positions of the watermark messages. Then, we use an XD (multi-dimensional) injection head to inject multi-modal messages into sorted 3DGS points. To restore watermarking messages, a learnable gate is developed to recognize the watermarked locations, from which our XD-extraction heads are used to restore hidden messages. X-SG$^2$S is the first framework to unify 1D-to-3D watermarking and enable simultaneous multi-modal watermark embedding in 3DGS, achieving this with minimal rendering interference and zero modifications to parameters or pipelines. Extensive experiments demonstrate that X-SG$^2$S effectively preserves consistency between the watermark and the original 3DGS, exhibits robustness against model degradation, and maintains accurate judgment capabilities.

cs.CR↗

Delay-Doppler Sensing Performance Analysis for MIMO-OFDM ISAC Systems

In communication-centric integrated sensing and communication (ISAC), delay-Doppler sensing reuses data-bearing orthogonal frequency division multiplexing (OFDM) signals rather than dedicated radar probing waveforms. Consequently, the resulting range-Doppler map (RDM) is shaped not only by target parameters, but also by communication-symbol randomness and, in multi-antenna transmissions, by spatial beamforming. While existing analyses have largely focused on single-antenna OFDM-ISAC, the delay-Doppler sensing behavior of multi-antenna OFDM-ISAC remains insufficiently understood. This paper analyzes a multi-input multi-output (MIMO)-OFDM-ISAC system in which multiple data streams jointly illuminate a sensing target. We derive second-order moment expressions for the RDM under matched filtering (MF) and reciprocal filtering (RF), and use them to characterize the dynamic range (DR). The analysis reveals two key multi-stream effects. First, under MF, the random superposition of multiple beamformed streams creates an additional RDM floor beyond the modulation-dependent and receiver-noise terms; therefore, constant-modulus signaling no longer eliminates the data-induced pedestal as in single-antenna OFDM-ISAC. Second, under RF, the matched-angle data-induced floor is removed, but the noise floor is amplified according to the reciprocal-power statistics of the beamformed target illumination. These results show that the user-target angular geometry directly governs the MF/RF tradeoff: MF is more robust under weak illumination, whereas RF can achieve a higher DR when reciprocal-noise amplification is mild. Numerical results validate the analysis and demonstrate the distinct geometry-dependent behaviors of MF and RF.

eess.SP↗

Langevin Theory of Non-Markovian Quantum Dynamics: Application to Delayed Coherent Feedback and the Laser Linewidth

Phase-space methods are powerful tools for the treatment of Markovian open quantum systems: they map the reduced dynamics of a system S, in interaction with an environment E, exactly onto Langevin equations for c-number stochastic variables, as opposed to Heisenberg-Langevin equations for operators. Langevin equations provide analytical insight in key regimes and excel at handling strong nonlinearities and couplings, where other methods often falter. Extending phase-space methods to non-Markovian dynamics, however, has remained a long-standing challenge. Here we address this gap by applying phase-space representations to the full S+E system; integrating out the environmental degrees of freedom then yields a general Langevin framework for S that incorporates both deterministic and stochastic contributions from E. Normally ordered representations, such as the Glauber-Sudarshan P representation and its positive variant due to Drummond and Gardiner, lead to Langevin equations in which (i) non-Markovian effects emerge exclusively in the deterministic terms, via a memory kernel, and (ii) noise contributions vanish when E is initially in the vacuum state. To demonstrate the power of this framework, we address the paradigmatic problem of delayed coherent feedback, in which the system is driven by its own past state, and study its impact on the laser linewidth: we recover the narrowing observed well above threshold and predict an enhanced narrowing just above it. Crucially, the number of stochastic variables scales linearly with the system size, making the framework suitable for problems ranging from a few degrees of freedom to genuinely many-body systems. This opens the way to the systematic study of non-Markovian driven-dissipative quantum systems using the same analytical and numerical tools that have long made phase-space methods so successful in the Markovian regime.

quant-ph↗

Approximating the peculiar velocity distribution of dark matter halos with Tsallis statistics

Dark matter halos, which host galaxies and galaxy clusters, have peculiar velocities far from thermal equilibrium. Characterizing the nonlinear and non-Gaussian features of this velocity distribution improves our understanding of the gravitational evolution of the cosmic web and supports related cosmological applications. We endeavor to establish a connection between the peculiar velocity distribution of halos and nonequilibrium statistical mechanics, with the objective of obtaining a model that is both concise and accurate for practical applications. We extracted halo samples from large N-body simulations and performed maximum-likelihood fits to the peculiar velocity distributions using a two-parameter Tsallis model, derived from non-extensive statistical mechanics. On the theoretical side, we reformulated the halo distribution in the superstatistics framework by means of a generalized Gram-Charlier expansion based on the gamma distribution. For halo peculiar velocities below 1000 km/s, the Tsallis model achieves 5 percent accuracy over z=0-2, with performance improving toward lower redshifts. Our results show that the halo velocity distribution becomes increasingly non-Gaussian and departs further from equilibrium over time. The best-fit parameters depend only weakly on mass, though low-mass halos exhibit slightly stronger non-Gaussianity. The two parameters, especially the velocity dispersion, offer promising probes of cosmological parameters. Theoretically, we find that in general the halo peculiar velocity distribution function is expressible as a superposition of a series of Tsallis distribution functions, while simulation results demonstrate that the zeroth-order approximation, namely a single Tsallis function, already achieves sufficient accuracy.

astro-ph.CO↗

An Axial $U_A(1)_{L_μ-L_τ}$: UV Completion and Experimental Searches

We propose an anomaly-free and renormalizable axial $U_A(1)_{L_μ-L_τ}$ model and study its experimental signatures for $A'$ masses from the MeV scale to the TeV scale. The opposite charges of the left- and right-handed charged leptons forbid the usual muon and tau Yukawa interactions. Their masses are instead generated by a singlet scalar and heavy vector-like leptons through a universal-seesaw mechanism. We focus on heavy vector-like leptons, small light--heavy mixing, and $m_s\gtrsim10~\mathrm{GeV}$. In this limit, the observables considered here depend mainly on $(m_{A'},g_X)$, while the other model parameters are restricted by mixing and perturbativity. We confront this benchmark with current experimental searches. For neutrino trident production, our finite-$m_μ$ calculation shows that $A'$ modifies the axial weak coefficient, rather than the vector coefficient relevant to the usual $L_μ-L_τ$ model. The longitudinal mode enhances muon bremsstrahlung and gives a negative contribution to $(g-2)_μ$; the latter dominates over the scalar contribution in our benchmark. Combining these results with invisible meson decays and four-muon resonance searches, we summarize the phenomenological constraints in the $(m_{A'},g_X)$ plane. For $m_{A'}\gg m_μ$, vector and axial final-state-radiation rates become nearly identical, so the corresponding collider limits can be obtained by rate matching. At a future muon collider, the total rate alone does not fully resolve the interaction structure, whereas angular distributions, especially the forward--backward asymmetry in $μ^+μ^-\toτ^+τ^-$, retain direct sensitivity to chirality.

hep-ph↗

Towards World Models in Biomedical Research

A central goal of biomedicine is to understand, predict and ultimately control the dynamic mechanisms by which biological systems respond to perturbations, disease progression and therapeutic intervention. Although foundation models and large language models have accelerated biomedical data interpretation, most current systems remain focused on static pattern recognition rather than prospective simulation of biological futures. Here we propose biomedical world models as a paradigm for AI-driven discovery. These models learn latent representations of molecular, cellular, tissue and clinical states, together with intervention-conditioned dynamics that allow future trajectories to be simulated before actions are taken. We discuss how biomedical world models could function as data engines, environment simulators and scientific planning substrates across applications including virtual cells, organoids, virtual patients and surgical simulation. We outline the data infrastructure, evaluation benchmarks, safety constraints and governance frameworks required. Biomedical world models may provide a foundation for simulation-guided, closed-loop and experimentally actionable biomedical discovery.

cs.AI↗

Validation of HRV Studio: A Transparent and Quality-Control-Aware Platform for Heart Rate Variability Analysis

Reproducibility of heart rate variability (HRV) analysis is limited by differences in preprocessing and computational conventions across software platforms. We developed HRV Studio, an open-source PyQt6-based desktop application integrating transparent HRV analysis with automated quality-control (QC) diagnostics. Validation included large-scale agreement with NeuroKit2, targeted Kubios benchmarking, spectral-method comparison, synthetic perturbation testing, recording-duration sensitivity analysis, and arrhythmia-focused QC stress testing. HRV Studio showed near-identical agreement for the widely used time-domain indices RMSSD and SDNN under matched conditions. In the primary five-minute NeuroKit2 comparison, frequency-domain median relative errors were 1.35% for LF, 0.18% for HF, and 1.41% for LF/HF, while VLF remained more convention-sensitive (37.79%). Nonlinear Poincaré indices also demonstrated high consistency. Sequence-harmonized Kubios benchmarking confirmed near-identical agreement for time-domain and nonlinear indices and strong agreement for most frequency-domain measures. Extended ten-minute analyses reproduced the same overall pattern with lower disagreement for some convention-sensitive spectral outputs. Synthetic and arrhythmia stress tests maintained 100% numerical stability while consistently triggering QC warnings. Overall, HRV Studio provides a transparent and reproducible platform for HRV research, with strong cross-platform consistency when NN sequences, preprocessing, and analytical conventions are harmonized. Stress-test results indicate computational robustness rather than clinical validation.

physics.med-ph↗

A Novel Decoupling Method for Investigating Distinct Domain Evolution in Ferroelectric Film

Conventional polarization characterization techniques provide only the averaged response of ferroelectric films, limiting the investigation of domain-dependent reliability mechanisms in HfO2-based ferroelectrics. In this work, a new domain decoupling method is proposed to separately analyze ferroelectric domains with distinct switching behaviors. A three-domain model consisting of upward non-switchable domains, downward non-switchable domains, and switchable domains (Pd) is first introduced to describe heterogeneous domain populations during electrical cycling. By combining complementary switching-current measurements, the responses of different domain populations can be selectively extracted and reconstructed. The proposed method enables quantitative tracking of individual domain populations during cycling. As a demonstration, the method is further applied to analyze the time-dependent evolution of domain populations after electrical cycling. This approach provides a new route for investigating imprint, fatigue, and other reliability-related phenomena in ferroelectric devices.

cond-mat.mtrl-sci↗

Halo Mass of ULIRGs at Cosmic Noon

We present a clustering analysis of $\sim 3000$ ultraluminous infrared galaxies (ULIRGs) at $z\sim 2$, uniformly selected by $24μ{\rm m}$ flux and IRAC colors in the COSMOS and BOOTES fields. We measure the angular correlation functions of ULIRGs in both fields and fit them with galaxy clustering models. Linear theory modeling shows that these ULIRGs reside in dark matter halos with characteristic masses of $\log M_{\rm h} /({h^{-1}\rm M_{\odot}}) = 12.64\pm 0.50$ in COSMOS and $12.72\pm 0.13$ in BOOTES. The halo occupation distribution (HOD) modeling yields occupation-weighted effective halo masses of $\log M_{\rm eff} /({h^{-1}\rm M_{\odot}}) =12.50^{+0.26}_{-0.28}$ for COSMOS and $12.89^{+0.11}_{-0.12}$ for BOOTES. These host halos are expected to evolve into halos with masses of $\sim 10^{13.6-13.9}\ {h^{-1}\rm M_{\odot}}$ at $z=0$. The HOD fits allow for a non-negligible satellite contribution to the clustering of galaxies in the BOOTES field, but the satellite fraction derived in the COSMOS field appears nearly zero.

astro-ph.CO↗

A Quantitative Approximation Framework for Flow Distillation in Diffusion Models

We develop a quantitative framework for diffusion distillation by viewing few step sampling as approximation through compositions of learned flow maps. For trajectory distillation of the probability flow ODE, we show that low noise multimodal regimes separate score approximability from dynamical stability: the score remains efficiently approximable, while small local errors may be strongly amplified by stiff flow dynamics. In a Gaussian mixture Ornstein--Uhlenbeck model, we prove time uniform \(L^p(p_t)\) score approximation by ReLU and ReQU networks with explicit polylogarithmic complexity, and derive a computable Lipschitz bound \(L(t)\) for the flow velocity. The stability factor \(\exp\bigl(\int_s^t L(u)\mathrm du\bigr)\) can grow exponentially as noise decreases and mixture separation increases. Comparing this certificate with a certified local Lipschitz budget for one step students identifies regimes of direct distillation difficulty, without implying an approximation lower bound. We also show that deep residual compositions control global transport error through propagated local errors, and that equalizing cumulative stability yields an optimal nonuniform segmentation. With eight segments, this grid reduces final mean relative MSE by up to \(51.9\%\) versus uniform grids.

stat.ML↗