arXiv subjects

Lei Liu

Publications and source records attributed to Lei Liu.

At least 37 records · Page 2Linked to original sources

arXiv2026

From Discrete Trailing Returns to a Continuous Graphical Profile: Return-to-Present Curves

Investment performance is commonly presented either as a conventional cumulative-return chart, which fixes a historical starting date and traces performance forward, or as a trailing-return table, which fixes the current endpoint but reports only a small set of prespecified horizons. These two displays have complementary limitations: fixed-start comparisons are conditional on the selected origin, whereas trailing returns provide only discrete snapshots of the underlying fixed-endpoint return function. We present the Return-to-Present (RTP) curve as a continuous fixed-endpoint representation that brings these perspectives together by holding the evaluation date fixed while allowing the hypothetical historical purchase date to vary over the available history. Familiar 1-month, 3-month, 6-month, 1-year, and longer trailing returns therefore become selected points on a continuous curve. When multiple investments are overlaid, RTP directly displays entry-date sensitivity, persistent relative advantage, crossings, and the timing and magnitude of separation without requiring selection of a single historical origin. The same endpoint-based construction naturally accommodates investments with unequal inception dates, recurring purchases, and retrospective portfolio rotation decisions in which sale and replacement-purchase dates may differ. We illustrate these uses with real investment data and discuss its relationship to momentum. RTP does not define a new return measure; its contribution is a simple graphical organization of familiar realized returns for historical comparison and decision support rather than prediction or statistical inference.

stat.AP↗

arXiv2026

Spectral-Efficient MIMO-OFDM: Low-Complexity Solution based on Random Multiplexing

This paper presents a low-complexity precoded MIMO-OFDM system for achieving improved spectral efficiency (SE) via intentionally compressing information symbols among subcarriers. Particularly, the proposed scheme leverages the powerful random multiplexing mechanism for precoding, and adopts the linear-complexity orthogonal approximate message passing (OAMP) estimator for symbol detection, where the compatibility with the existing fifth generation (5G) architectures is fully preserved. We further provide the theoretical analysis based on the replica-symmetric (RS) formula. This analysis confirms the advantages of the proposed system with respect to the adopted compression ratios, where an interesting phase transition behavior is verified. Numerical results coincide with our analysis and demonstrate significant improvements in terms of achievable rates and bit error rate (BER) compared to conventional MIMO-OFDM counterpart, making the proposed scheme a promising solution to 6G and beyond wireless networks.

eess.SP↗

arXiv2026

M$^3$ISR: A Multi-Modal Multi-View Benchmark for 3D/4D Gaussian Splatting and Feedforward Compression

High-fidelity free-viewpoint video (FVV) and interactive rendering increasingly rely on explicit Gaussian representations, yet practical deployment remains constrained by representation size, dynamic updates, and computational cost. Existing multi-view video benchmarks provide valuable real-captured content, but they make it difficult to isolate the effects of controlled camera geometry, representation efficiency, and temporal redundancy. We introduce M$^3$ISR, a controlled synthetic benchmark for 3D and 4D Gaussian Splatting (3DGS/4DGS). The benchmark contains 25 scenes from five indoor and outdoor scene groups, two camera/motion configurations, six synchronized 1080p views, and dense ground-truth annotations including RGB, camera parameters, depth, semantic and instance segmentation, and static--dynamic masks. The shared-center camera design intentionally isolates angular view variation and enables controlled evaluation of novel-view synthesis and representation efficiency. We organize M$^3$ISR into five complementary tracks covering 3DGS synthesis, 4DGS synthesis, 4DGS streaming, 3DGS compression, and 4DGS compression. Representative baseline results show small differences in static reconstruction quality but substantial differences in representation storage, while the evaluated streaming methods exhibit substantially higher reported training or reconstruction cost than the corresponding offline dynamic reconstruction baselines. We further define feedforward compression tasks for 3DGS and 4DGS and provide reference rate--distortion formulations and preliminary baseline evaluations. The benchmark is intended as a controlled and complementary testbed for systematic study of Gaussian-based FVV reconstruction, compression, and streaming.

cs.CV↗

arXiv2026

Gate-imprinted memory and light-induced erasure of superconductivity at KTaO$_3$-based interfaces

Realizing non-volatile control of superconductivity is a key step toward integrating memory and quantum functionality in future information technologies. KTaO$_3$-based heterostructures uniquely host interfacial two-dimensional superconductivity and quantum paraelectric lattice background. The coupling between these two degrees of freedom potentially provides a promising route to encode memory into the superconducting state. Here we reveal two intertwined phenomena in AlO$_x$/KTaO$_3$ heterostructures: a gate-imprinted memory in which electrostatic gate cycling promotes superconductivity, and its erasure by optical illumination at cryogenic temperatures. These phenomena arise from a previously unrecognized interplay between the superconducting interface and emergent lattice excitations including polar-nanoregion reorientation and charge trapping/detrapping by oxygen vacancies. These results demonstrate configurable superconductivity at correlated oxide interfaces, opening a pathway to enrich dissipationless transport with non-volatile controls for superconducting elements.

cond-mat.supr-con↗

arXiv2026

DepTGL: A Parallel Framework for Memory-based TGNN Training with Adaptive Temporal Data Dependency Management

Memory-based Temporal Graph Neural Networks (M-TGNNs) maintain recursively updated node states to capture fine-grained temporal interactions. However, existing distributed frameworks lack effective mechanisms for managing the temporal data dependencies inherent in these models. As a result, they must enforce strict chronological updates, incur substantial remote synchronization overhead, and experience severe load imbalance when temporal event streams are skewed. We propose DepTGL, a scalable distributed training framework that restructures temporal-dependency management for M-TGNNs from a data-centric perspective. First, DepTGL introduces a hybrid temporal-dependency management scheme that explicitly balances communication and caching overhead via temporal-event caching, supplemented by selective dependency-driven communication. Next, DepTGL incorporates a gradient-aware cache-synchronization policy that adaptively suppresses boundary updates as model optimization stabilizes, thereby reducing redundant synchronization. Finally, DepTGL integrates a load-aware temporal-pruning strategy that eliminates auxiliary replay events under skew-induced load spikes, reducing redundant data processing and mitigating straggler effects. Experiments on six real-world temporal graphs show that DepTGL achieves an average speedup of 4.99x over state-of-the-art baselines, while maintaining comparable accuracy.

cs.DC↗

arXiv2026

Beyond Sgr A* and M87*: Sub-Microarcsecond Black Hole Shadow Detection via Lunar-based Extremely Long Baseline Interferometry

The 1.3 mm ground-based very long baseline interferometry (VLBI) array Event Horizon Telescope (EHT), is limited by Earth's diameter, restricting horizon-scale imaging to only M87* and Sgr A*. Extending baselines to the Moon would achieve ~0.7 microarcsecond angular resolution at 230 GHz, enabling shadow detection for many more supermassive black holes (SMBHs). The concept is motivated by space VLBI missions and lunar exploration, including the ongoing Lunar Orbit VLBI EXperiment (LOVEX) aboard QueQiao-2 (Chang'E-7) and the planned International Lunar Research Station (ILRS). We assess shadow detectability for 31 SMBHs with predicted large angular sizes, assuming optically thin emission at 230 GHz, exploring different telescope locations and antenna sizes. Assuming a telescope at the lunar antipode, we simulate the Moon-Earth (u,v) coverage and show that sources near the Moon's orbital plane yield projected baselines spanning a wide range, enabling sampling of the first visibility null - a key shadow signature. Using a geometric ring model, we identify six shadow-detectable candidates: M104, NGC 5077, and NGC 1052 are detectable with a 5 m lunar-based telescope; PGC 049940 with 10 m; NGC 524 with 20 m; and NGC 5252 with 40 m. If additional space telescopes fill the baseline coverage gaps between Moon and Earth, 14 candidates are detectable for the n=1 photon-ring region with a lunar-based telescope up to 40 m. These results provide a clear scientific and technical motivation for lunar-based telescopes in future black hole shadow studies.

astro-ph.GA↗

arXiv2026

A Universal Random Precoding Framework for MIMO Systems

Current wireless systems combat inter-symbol interference (ISI) by diagonalizing or sparsifying the channel matrix, yet they remain vulnerable to selective fading. To address this, we propose a universal random precoding (RP) transmission framework based on the universality class. RP leverages random transforms to statistically exploit all subchannels and construct an equivalent channel belonging to the universality class, thereby enhancing diversity gain while maintaining backward compatibility with existing waveforms. Low-complexity implementations include the randomly permuted fast transform (FT-RP) and the interleaved block-sparse fast transform (IBSFT-RP). A cross-domain OAMP/MAMP (CD-OAMP/MAMP) detector is designed for RP systems, which is replica maximum \textit{a posteriori} (MAP)-optimal according to state evolution (SE). Simulation results on MIMO systems demonstrate that RP with CD-OAMP/MAMP achieves near-RM performance with much lower complexity, with additional benefits of flexible compression ratios for spectral efficiency.

eess.SP↗

arXiv2026

ConMem: Contribution-Aware Memory for Long-Horizon Manufacturing Inspection Logs

Long-horizon steel-equipment inspection requires reasoning over heterogeneous records accumulated across repeated inspection cycles. Existing retrieval-augmented generation systems treat historical logs as a static corpus and retain records without estimating their diagnostic value, failing to report early risk. To this end, we propose ConMem, a contribution-aware memory framework for LLM-assisted equipment inspection, supporting a human-in-the-loop early-risk screening system. Specifically, our ConMem first segments inspection logs into functional evidence units, then estimates each memory unit's contribution to downstream diagnosis through a Shapley-style estimation, and finally retains high-value evidence under a constrained memory budget. In experiments, we evaluate ConMem on real-world dataset and ConMem achieves 76.0% QA accuracy, exceeding the strongest directly comparable baseline. Relative to the naive 8K-context LLM baselines, it reduces the average number of input tokens by 88.2% and response time by 86.6%. Ablation studies also show that the functional-role-aware segmentation and contribution-based valuation are helping prioritize weak degradation signals for targeted field inspection. Practical deployments further confirm that ConMem retains the weak early signal across three inspection cycles, providing an early-stage seal-wear alert targeted for on-site inspectors.

cs.AI↗

arXiv2026

Memory AMP: Overflow Avoidance, Complexity Reduction, and Comparative Analysis

Approximate message passing (AMP)-type algorithms are widely used for signal recovery in high-dimensional noisy linear systems. Recently, a framework called memory AMP (MAMP) was introduced, offering a new approach to incorporating memory terms within AMP algorithms. Building on this, a low-complexity gradient descent MAMP (GD-MAMP) was proposed for right-unitarily invariant matrices. In this paper, we first address an overflow problem in GD-MAMP caused by intermediate variables exceeding the floating-point range, which typically occurs when the condition number is large. Second, we propose two low-complexity variants of GD-MAMP: one replaces full-length memory with partial memory, while the other reduces the number of matrix-vector products per iteration by $1/3$ (from three to two). Neither degrades the convergence speed notably. Third, we develop a general gradient-based formulation for designing MAMP algorithms. This formulation recovers warm-started conjugate gradient VAMP (WS-CG-VAMP) as a special case. Furthermore, we show that the computation of the orthogonalization parameters in this formulation can suffer from catastrophic cancellation, which explains the finite-precision instability of WS-CG-VAMP. Finally, we derive an equivalent reformulation, termed WS-CG-VAMP(r), which reduces the number of matrix-vector products by up to $50\%$. Measured by matrix-vector products, GD-MAMP converges faster for small condition numbers, whereas WS-CG-VAMP(r) converges faster for large ones under high-precision arithmetic but may diverge in IEEE double precision due to catastrophic cancellation.

cs.IT↗

arXiv2026

Versatile Video Representation via Feed-Forward 2D Gaussian Splatting Tokenization

Recent video representation methods that rely on fixed-grid, patch-wise tokenization often exhibit limited versatility.Spatially, uniformly allocating a fixed number of tokens often leads to over-encoding in low-information regions. Temporally, reducing redundancy remains challenging without explicitly distinguishing between static and dynamic content. In this work, we introduce the Gaussian Video Transformer (GVT), a versatile video representation framework built on a feed-forward 2D Gaussian Splatting (2DGS) tokenization scheme. We first extract latent rigid features from a video clip and represent them with a set of 2D Gaussians generated by our proposed Spatio-Temporal Gaussian Embedding (STGE) mechanism in a feed-forward manner. Such 2D Gaussians not only enhance spatial adaptability by assigning higher (resp., lower) rendering weights to regions with higher (resp., lower) information content during rasterization, but also improve generalization by avoiding per-video optimization. To enhance the temporal versatility, we introduce a Gaussian Set Partitioning (GSP) strategy that separates the 2D Gaussians into static and dynamic sets, which explicitly model static content shared across different time-steps and dynamic content specific to each time-step, enabling a compact representation. We evaluate GVT across four tasks: video reconstruction, video action recognition, video compression, and video generation, on the UCF101, Kinetics, and DAVIS datasets. The results demonstrate state-of-the-art reconstruction and compression performance, improved action recognition, and video generation performance comparable to the baseline MAGVIT-v2.

cs.CV↗

arXiv2026

Testing Microbiome Community Differences in High Dimensions: A Bootstrap Approach for Compositional Data

Understanding differences in microbial community structure is critical for uncovering risk factors and mechanisms underlying diseases such as colorectal cancer and preterm birth. Microbiome data present unique statistical challenges because they are compositional in nature, violating assumptions of many classical inference procedures. We propose an empirical bootstrap framework that enables robust hypothesis testing for equality of microbial community means across groups, including two-sample, paired, and multi-sample settings. The method accounts for the simplex structure of microbiome data and provides valid inference even in high-dimensional regimes. Through applications to two large-scale studies, fecal microbiota in colorectal adenoma and cancer patients, and vaginal microbiota in pregnancy with preterm birth outcomes-we demonstrate that our approach identifies clinically meaningful differences that conventional methods fail to detect, such as age-related differences in adenoma prevalence and race-associated disparities in vaginal microbiome composition. These results highlight the potential of resampling-based inference for advancing microbiome research, improving reproducibility, and uncovering clinically relevant microbial signatures.

stat.ME↗

arXiv2026

Joint Model for Mediation Analysis with Causally Related Longitudinal and Recurrent Event Mediators for Survival Outcome

Recurrent events and repeated measures are commonly encountered in clinical longitudinal studies, often holding strong associations with patient outcomes. Although joint models for repeated measures, recurrent events, and a terminal event have been developed to account for their correlation, limited methodologies exist to examine causal mediation mechanisms involving multiple types of mediators, especially when mediators are causally related. This study addresses this gap by proposing a novel causal mediation analysis framework to quantify natural direct and indirect effects when both recurrent events and repeated measures act as mediators with causal dependencies. We extend joint modeling approaches by incorporating shared random effects (frailties) structures, relaxing the commonly used ``sequential ignorability" assumption, and accounting for unmeasured time-independent confounders through shared random effects. We apply our method to the Terry Beirn Community Programs for Clinical Research on AIDS (CPCRA) study and demonstrate that both recurrent opportunistic infections (OIs) and repeated CD4 measurements mediate the effects of prior AIDS-defining conditions on survival outcomes. Additionally, the shared random effects between repeated CD4 and survival models highlight the presence of unmeasured confounding between CD4 counts and mortality. Simulation studies demonstrate the robustness and finite sample performance of our estimators for natural direct and indirect effects. The proposed methodology enables a more comprehensive investigation of causal pathways in longitudinal studies with multiple mediators, providing insights into treatment mechanisms and informing clinical decision-making.

stat.ME↗

arXiv2026

LLM-Extracted Covariates for Clinical Causal Inference: Rethinking Integration Strategies

Causal inference from electronic health records (EHR) is fundamentally limited by unmeasured confounding: critical clinical states such as frailty, goals of care, and mental status are documented in free-text notes but absent from structured data. Large language models can extract these latent confounders as interpretable, structured covariates, yet how to effectively integrate them into causal estimation pipelines has not been systematically studied. Using the MIMIC-IV database with 21,859 sepsis patients, we compare seven covariate-integration strategies for estimating the effect of early vasopressor initiation on 28-day mortality, spanning tabular-only baselines, traditional NLP representations, and three LLM-augmented approaches. A central finding is that not all integration strategies are equally effective: directly augmenting the propensity score model with LLM covariates achieves the best performance, while dual-caliper matching on text-derived categorical distances restricts the donor pool and degrades estimation. In semi-synthetic experiments with known ground-truth effects, LLM-augmented propensity scores reduce estimation bias from 0.0143 to 0.0003 relative to tabular-only methods, and this advantage persists under substantial simulated extraction error. On real data, incorporating LLM-extracted covariates reduces the estimated treatment effect from 0.055 to 0.027, directionally consistent with the CLOVERS randomized trial, and a doubly robust estimator yielding 0.031 confirms the robustness of this finding. Our results offer practical guidance on when and how text-derived covariates improve causal estimation in critical care. Our code is available at https://github.com/fpxlei/LLM-Covariates-Causal.

cs.LG↗

arXiv2026

Evidence-Grounded AI for Musculoskeletal Care

Musculoskeletal diseases are among the leading causes of disability and drive the greatest global need for rehabilitation. Because recovery, remodelling and degeneration of bones, joints and related tissues unfold over months to years, care requires longitudinal management rather than isolated decisions. Clinicians must repeatedly integrate evolving patient evidence, medical knowledge and stage-specific functional goals, yet evidence is often fragmented across visits, departments and hospital systems, disrupting continuous, individualised management. Here we report OrthoPilot, a clinical artificial intelligence (AI) system powered by a large language model (LLM) that integrates hospital data streams with authoritative external knowledge for continuous musculoskeletal care. It autonomously retrieves real-time imaging, laboratory, pathology and order data and translates evolving patient states into evidence-based decisions from admission diagnosis through rehabilitation planning. We established a specialist-validated benchmark from real-world electronic health records (EHRs) spanning 1,000 disease codes. In a full-pathway reader study against 81 orthopaedic physicians, OrthoPilot outperformed experts with 25 years of experience in diagnostic reasoning, clinical decision-making and management planning. This advantage generalised across 60 external clinical centres, where OrthoPilot surpassed all evaluated intelligent systems. In a prospective physician decision-making study of 1,870 complex cases, OrthoPilot improved full-chain management success by 10.6%. In a randomised deployment involving 8,240 inpatients, integration into routine care increased cumulative cases per bed by 9.7% and improved patient-reported access to health information. These results move clinical AI from predicting isolated events toward executing longitudinal management across complete musculoskeletal care pathways.

cs.AI↗

arXiv2026

Mutual Debiasing via Dual-Seed Comparison for Probabilistic Sampling in Large Language Models

Although Large Language Models (LLMs) demonstrate remarkable capabilities in reasoning and decision-making, high-fidelity probabilistic sampling remains a persistent challenge. When generating random variables, LLMs consistently exhibit systematic biases that warp the target probability distributions. Current approaches often rely on a single, self-generated seed, which inherits model-specific biases. To overcome this vulnerability, we introduce Dual-Seed Comparison (DSC), a transparent, tool-free protocol that utilizes two independent LLM-generated seeds to neutralize bias. DSC compares the character-level ordinal values of the two seeds to construct a bit sequence, converts and normalizes this sequence into a pseudo-uniform variate, and then maps the variate to the target distribution through the inverse cumulative distribution function (CDF). Empirical results show that DSC substantially outperforms existing methods across 96\% of evaluated settings. Beyond direct sampling, task-adapted variants based on the DSC comparison operator improve distributional control in MCQ generation and attribute-constrained text-to-image prompting.

cs.CL↗

arXiv2026

Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities

Current benchmarks for evaluating Large Language Models (LLMs) in data analysis often fail to reflect real-world settings. They typically focus on fact retrieval from small tables and overlook the challenges of large multi-tabular datasets, external knowledge integration, and exploratory insight discovery. We introduce DataGovBench, a benchmark derived from governmental open data designed to evaluate LLMs in practical scenarios. The benchmark includes two tasks: Table QA that requires solving complex decomposable questions and producing textual answers or visualizations, and Table Insight that evaluates the ability of models to generate expert-level findings through exploratory data analysis. Comprehensive experiments with state-of-the-art LLMs, both with and without agentic frameworks, reveal significant performance gaps across both tasks. These results suggest that current LLM-based systems remain far from satisfying the demands of real-world data analytics. DataGovBench provides a challenging benchmark for advancing research on LLMs capable of both answering analytical queries and discovering insights from data. Code and sample data are available at https://github.com/SoHasegawa/datagovbench.

cs.CL↗

arXiv2026

FAST: A Holistic Framework for Optimizing Memory-I/O, Computation, and Sampling in Temporal GNN Training

Temporal Graph Neural Networks (TGNNs) are widely used for learning from dynamic graphs in applications such as recommendation, social network analysis, and traffic forecasting. However, scaling TGNN training to large dynamic graphs remains challenging due to three intertwined bottlenecks: memory I/O, irregular computation, and temporal neighbor sampling. Existing systems often optimize these stages in isolation, leaving substantial performance headroom on the table. We present FAST, a holistic framework that accelerates end-to-end TGNN training by jointly optimizing sampling, memory I/O, and computation. FAST introduces SlimCache, which exploits within-batch compression and cross-batch caching to reduce host-device data movement under limited GPU memory budgets. It further designs thread-efficient graph operators tailored to sparse temporal subgraphs, improving GPU cache locality and reducing the latency of aggregation and edge softmax. In addition, FAST employs a topology-aware sampling strategy that improves CPU cache locality and accelerates temporal neighbor sampling. Extensive experiments on real-world large dynamic graphs show that FAST achieves an average of 2.1x (up to 4.7x) speedup over state-of-the-art systems without sacrificing model accuracy.

cs.LG↗

arXiv2026

Capacity-Region-Achieving Sparse Regression Codes for MIMO Multiple-Access Channels

This paper proposes a coding framework for capacity-region-achieving sparse regression (SR) codes over MIMO multiple-access channels (MIMO-MAC), where a single SR code is used for each user at the transmitter. With random semi-unitary dictionary matrices applied for encoding, multiple-access OAMP (MA-OAMP) enables reliable parallel interference cancellation (PIC) at the receiver. Theoretically, an optimal coding principle with the MA-OAMP receiver, which achieves the sum capacity and, in combination with time sharing, achieves the entire capacity region, is established as the guiding principle for designing capacity-region-achieving codes. Accordingly, a coding scheme for capacity-region-achieving SR codes is proposed via proper power allocation over the position-modulated signals.

cs.IT↗