arXiv subjects

Ang Li

Publications and source records attributed to Ang Li.

At least 73 records · Page 4Linked to original sources

arXiv2026

Identification of Probabilities of Causation: from Recursive to Closed-Form Bounds

Probabilities of causation (PoCs) are fundamental quantities for counterfactual analysis and personalized decision making. However, existing analytical results are largely confined to binary settings. This paper extends PoCs to multi-valued treatments and outcomes by deriving closed form bounds for a representative family of discrete PoCs within Structural Causal Models, using standard experimental and observational distributions. We introduce the notion of equivalence classes of PoCs, which reduces arbitrary discrete PoCs to this family, and establish a replaceability principle that transfers bounds across value permutations. For the resulting bounds, we prove soundness in all dimensions and empirically verify tightness in low dimensional cases via Balke's linear programming method; we further conjecture that this tightness extends to all dimensions. Simulations indicate that our closed form bounds consistently tighten recent recursive bounds while remaining simpler to compute. Finally, we illustrate the practical relevance of our results through toy examples.

cs.AI↗

arXiv2026

OpenDPDv2: A Unified Learning and Optimization Framework for Neural Network Digital Predistortion

Neural network (NN)-based Digital Predistortion (DPD) improves linearization for wideband radio frequency (RF) power amplifiers (PAs) but often increases the complexity of the digital back-end. This paper presents OpenDPDv2, an open-source end-to-end framework that unifies PA modeling, NN-DPD learning, and deployment-oriented optimization. OpenDPDv2 introduces temporal residual (TRes)-Delta Gated Recurrent Unit (DeltaGRU), a delta-RNN DPD architecture with a lightweight temporal residual path for robust operation under aggressive temporal sparsity, and supports joint optimization with fixed-point quantization. On a 3.5 GHz GaN Doherty PA driven by a TM3.1a 200 MHz 256-QAM OFDM signal, the FP32 TRes-DeltaGRU model achieves -59.9 dBc Adjacent Channel Power Ratio (ACPR) and -42.1 dB Error Vector Magnitude (EVM). With 56% temporal sparsity and W12A12 quantization, the model uses 450 active parameters while maintaining -51.8 dBc ACPR and -35.2 dB EVM. Timing-accurate Gem5-based evaluation further indicates a 4.5x reduction in forward-pass energy for this deployment-oriented operating point. Code, datasets, and documentation are publicly available at https://github.com/lab-emi/OpenDPD.

eess.SP↗

arXiv2026

An Agentic Generative Large Language Model for Treatment Planning of Colorectal Cancer

Treatment planning in precision oncology requires synthesizing heterogeneous patient information with rapidly evolving clinical guidelines to ensure guideline-concordant care. While large language models (LLMs) show promise in many diagnostic tasks, their adoption for high-stakes treatment planning is hindered by complex reasoning, adherence to timely clinical guidelines, and safety concerns. In this study, we present GatorOnco, an agentic LLM for colorectal cancer (CRC) treatment planning. GatorOnco is developed using a total of 282 billion tokens of biomedical text, including healthcare system-scale clinical text comprising 166 billion tokens from UF Health. We implemented a domain-adaptation method that integrates pre-training, model merging, a two-stage post-training approach, and agent-based reinforcement learning. An agentic retrieval-augmented generation (RAG) approach dynamically integrates time-sensitive clinical guidelines into the reasoning process. In a blind, randomized clinical evaluation conducted by five UF Health oncologists, GatorOnco significantly outperformed open-source LLMs (P < 0.01) and achieved expert-level performance comparable to UF Health oncologists. Compared with expert oncologists, GatorOnco received significantly higher ratings for readability (4.46 vs. 4.19, P < 0.01) and completeness (3.91 vs. 3.52, P < 0.01), while showing statistically comparable performance in correctness (4.09 vs. 4.11, P = 0.921), currency (4.04 vs. 3.98, P = 0.478), and safety (4.22 vs. 4.22, P = 0.999). These findings demonstrate that integrating agentic reasoning with large-scale domain adaptation can help bridge the gap for generative AI in high-stakes cancer treatment planning.

cs.CL↗

arXiv2026

BitFair: A 12-nm Bit-Serial CNN Accelerator with Learnable Early Termination and Adaptive Bit Ordering for Ultra-Low-Power XR Vision

Extended Reality (XR) wearables require always-on perception within tight power envelopes of a few watts and motion-to-photon latency budgets below 20 ms, leaving only a few milliseconds for neural-network inference. Bit-serial computing is attractive for such energy-efficient neural network acceleration, but many existing architectures still process all bits even when ReLU sets the final output to zero. This paper presents BitFair, a software-hardware co-designed bit-serial CNN accelerator with learnable bit-level early termination and adaptive bit ordering, working under the ultra-low-power and strict latency requirements of XR applications. BitFair exploits dynamic bit-level sparsity by learning per-layer thresholds that trigger early termination when partial sums reliably predict that the final ReLU output will be zero. Furthermore, it searches for layer-wise bit orders that prioritize informative bits, maximizing early termination without sacrificing accuracy. A GlobalFoundries 12-nm FinFET implementation with a core area of 0.34 mm^2, 104 KB on-chip memory, and voltage scaling from 0.55 to 0.70 V achieves sub-millisecond latency, up to 117.0 BTOPS/W, and 0.07 pJ/SOP. On IBM DVS128 Gesture and N-MNIST, BitFair achieves 96.5% and 97.7% accuracy, respectively, while improving effective energy efficiency by 4.0-22.1x and accuracy by up to 9.2% over prior fabricated XR vision accelerators.

cs.AR↗

arXiv2026

Understanding Reasoning from Pretraining to Post-Training

Reinforcement learning (RL) has become central to improving large language models (LLMs) on complex reasoning tasks, yet RL post-training is largely studied in isolation from the pretraining that precedes it. As a result, two basic questions remain open: (1) how do pretraining choices (model size, data) shape the returns to RL compute, and (2) what does RL actually do to the model? These questions are difficult to study in the standard LLM setting: pretraining corpora are vast and uncontrolled, making it hard to attribute behaviors to pretraining versus RL, and systematic compute sweeps across both stages are prohibitively expensive. To address these challenges, we use chess as a controlled testbed for studying reasoning across the full pretraining-to-post-training pipeline. We follow the standard LLM training pipeline by pretraining language models from 5M to 1B parameters on human chess games, supervised fine-tuning on synthetic reasoning traces, and running RL on chess puzzles with verifiable rewards. Using this framework, we find that the post-RL performance at given RL compute level is well-predicted from the pretraining loss, and slope of the RL reward curves improves approximately linearly with the pretraining tokens. Beyond scaling, we find that RL does not simply sharpen the SFT policy: on easy puzzles it amplifies correct moves the SFT policy already preferred, while on hard puzzles it surfaces correct moves that were nearly absent under SFT. We further test whether our findings transfer beyond chess by training a 1B language model on math-domain text, where the same predictive pattern emerges: longer-pretrained checkpoints reach higher post-RL performance and improve faster under RL. In sum, we provide a quantitative account of the pretraining-to-RL interface and a controlled testbed for studying the science of reasoning across the full pretraining-to-post-training pipeline.

cs.LG↗

arXiv2026

Efficient Discrete Position Design for Movable Antenna Systems: Low Complexity and Robustness

Building on advances in reconfigurable antenna techniques, movable antennas (MAs) can dynamically reshape antenna arrays and introduce additional spatial degrees of freedom (DoFs), thereby further improving communication performance. Despite these benefits, existing MA design algorithms often entail prohibitively high computational complexity from discrete positioning selection, which prevents practical implementations of MAs. In this paper, we investigate efficient solutions for the mutual information (MI) maximization problem of a multi-user multiple-input multiple-output (MU-MIMO) uplink communication system aided by discrete MAs. To this end, we first formulate the discrete MA positioning problem with the assumption of perfect channel state information (CSI). Then, we prove that the design problem falls into the category of monotone submodular maximization subject to a 2-system constraint. Accordingly, we propose a low-complexity distance-constrained submodular position search algorithm, which is theoretically shown to achieve at least 1/3 of the optimum. Furthermore, we extend our approach to scenarios with imperfect CSI, and show that the proposed submodular optimization-based design remains robust against channel estimation errors. Numerical results demonstrate that the proposed scheme can achieve at least 90% of the optimal solution's MI gain under both perfect and imperfect CSI assumptions. Remarkably, the algorithm achieves orders-of-magnitude complexity reduction (e.g., 34.4x faster than the branch-and-bound approach) while maintaining significant MI gains.

cs.IT↗

arXiv2026

Implementation of Split Deadlines in a Large CS1 Course

Office hour utilization in computer science courses can spike near deadlines, producing long wait times, frustrated students, and overworked staff. To address this problem, a large CS1 course implemented a split deadlines policy. Students were randomly divided into two groups with staggered release and due dates. Each group had the same amount of time to complete assignments, but the number of students with each due date was reduced by half. Our study evaluates the effectiveness of this policy. We measure office hour utilization and staff efficiency near deadlines, examine the policy's impact on student performance, and investigate student perception of the policy's fairness and effectiveness. Overall we found that the split deadline policy increased office hour efficiency, resulted in no significant difference in performance between groups, and was considered fair and effective by most students. Our experience report includes reflections and student feedback indicating how to implement and further improve similar policies.

cs.CY↗

arXiv2026

Characterization of Speech Imagery in Scalp EEG and Comparison with Motor Imagery

Speech imagery is an attractive brain-computer interface paradigm for communication because it is endogenous and intrinsically linguistic. Yet despite growing interest, its dominant scalp-EEG spatiotemporal characteristics remain poorly characterized. We investigated whether speech imagery, understood here as the motor imagery of articulatory movements, exhibits the motor-related mu/alpha and beta desynchronization expected from motor imagery. In $34$ participants, we compared speech imagery, finger motor imagery, and explicitly cued no-task trials recorded under the same trial structure, analyzing band-power dynamics across channels and time. Finger motor imagery showed the expected contralateral mu/alpha and beta desynchronization over sensorimotor areas, whereas speech imagery showed a weaker, more distributed increase in alpha power relative to no-task. A classifier discriminating imagery from no-task reached mean balanced accuracies of $0.563 \pm 0.071$ for speech imagery and $0.717 \pm 0.125$ for motor imagery, with band-ablation analyses showing larger, more robust alpha and beta effects for motor imagery. These results show that the dominant group-level scalp response to speech imagery did not resemble the canonical alpha/beta desynchronization associated with motor imagery.

eess.SP↗

arXiv2026

Global-Scale Self-Supervised Spatiotemporal Learning for NDVI Time-Series Reconstruction

Accurate and efficient reconstruction of cloud-contaminated and noise-corrupted NDVI time series remains a challenge in remote sensing. Deep learning provides a promising solution for modeling complex spatiotemporal dependencies; however, its application is often limited by the difficulty of obtaining paired clear-sky and degraded NDVI data for identical spatiotemporal locations. To address this issue, we propose GloSSR, a Global-scale Self-supervised Spatiotemporal framework for NDVI Reconstruction. The framework constructs supervisory signals by artificially degrading relatively clean NDVI observations with realistic cloud contamination patterns, producing self-supervised training pairs that closely mimic real-world degradation. It further introduces an end-to-end spatiotemporal learning network that jointly captures long-range temporal dependencies and short-term spatiotemporal correlation through a bidirectional Transformer with a ConvLSTM architecture. A temporal-channel attention-based reconstruction module is incorporated to enhance informative features, while a spatiotemporal prior constraint is designed to preserve both fine-scale structures and long-term phenological trends during optimization. Extensive evaluations on MODIS NDVI data demonstrate the effectiveness of the proposed framework across both artificial and real-world scenarios. In artificial degraded-pixel reconstruction experiments, GloSSR consistently outperforms the comparison methods. Time-series analyses based on real observations further demonstrate that the proposed framework can accurately characterize vegetation dynamics and capture the key phenological states. Long-term vegetation trend analysis and the transferability analysis to AVHRR data validate the scalability of the framework and illustrate its broad applicability for large-scale environmental monitoring.

cs.CV↗

arXiv2026

A Robotic System for Automated Manufacturing of Dielectric Elastomer Actuators

This letter presents an automated robotic manufacturing system for soft capacitors which operate as actuators and sensors. Emphasis is placed on the two processes that most directly govern device quality, dielectric layer formation by spin coating and carbon nanotube (CNT) electrode application by stamping. Twenty multilayer DEAs, each comprising 12 dielectric layers with a mean thickness of 55.37 +- 2.04 um and 11 alternating CNT electrodes, were fabricated reducing total process time by 14.2% and removing the operator from 56.1% of it.

cs.RO↗

arXiv2026

QR-Structured Thermal Triggers for Targeted Semantic Attacks on Infrared Vision-Language Models

Infrared vision-language models (IR-VLMs) extend thermal perception to open-vocabulary classification, image captioning, and visual question answering. However, their robustness to structured thermal perturbations and the stability of cross-modal semantic alignment remain insufficiently studied. We propose QR-Structured Thermal Triggers (QR-STT), a stealthy, training-free, black-box framework for targeted semantic steering of IR-VLMs. QR-STT preserves the functional regions of a QR pattern while optimizing its internal modules, each of which is assigned a cold, neutral, or hot thermal state. The framework jointly searches module topology and rendering parameters, including position, scale, rotation, intensity, blur, and roundness. A three-stage gradient-free procedure with greedy module-flip refinement efficiently handles the mixed discrete and continuous search space. The objective promotes alignment with an attacker-selected target, suppresses source-class evidence, and regularizes QR structure and visual similarity. Experiments on multiple CLIP-style encoders show that QR-STT consistently redirects image-text alignment toward chosen concepts while maintaining visual stealth. Perturbations optimized for classification also transfer to image captioning and VQA, causing target-consistent semantic drift in generated outputs. These results identify QR-structured thermal patterns as an interpretable attack surface for language-driven infrared perception and highlight the need for robustness evaluation against structured cross-task semantic attacks.

cs.CV↗

arXiv2026

From Semantics to Readout: Mechanistic Understanding of Audio Tokens after Fine-Tuning for Temporal Audio Grounding

Large audio-language models (LALMs) convey acoustic evidence to language decoders through native audio tokens, yet the internal roles of these tokens remain poorly understood. Using temporal audio grounding as a diagnostic setting, we examine how language-model fine-tuning affects the layerwise semantics, decoder accessibility, and temporal output alignment of native audio-token states through four complementary analyses: query-conditioned token semantics, calibrated token readout, temporal-window probes, and residual-delta erasure during generation. Alongside substantial improvements in temporal localization, semantic analysis of Qwen2.5-Omni shows that latent evidence for queried events is already present before fine-tuning and that the audio tokens most strongly aligned with the queried event appear at similar temporal positions before and after fine-tuning. After fine-tuning, event-related information in audio tokens becomes more accessible to the decoder, especially in early and middle layers, and a cross-checkpoint control shows that this improvement arises primarily from decoder adaptation. Temporal probes show that the base checkpoint already contains recoverable information about annotated windows and that fine-tuning mainly improves alignment with each checkpoint's own predicted temporal support. Residual-delta erasure further shows that removing audio-token updates within predicted windows harms timestamp generation more than removing the same number of randomly selected updates. The same broad improvements in decoder readability and prediction alignment also appear in Qwen2-Audio. Together, these results support a semantics-to-readout account in which grounding fine-tuning helps the decoder read existing event evidence and connect it more reliably to temporal outputs.

cs.SD↗

arXiv2026

An adaptive phase field framework for large-scale interface evolution problems using a strong-form gradient smoothing approach

Multiscale problems with evolving interfaces are ubiquitous in science and engineering. Phase-field models are a powerful tool for simulating interface-dominated phenomena in computational mechanics and materials modeling, but their application to large-scale problems is often constrained by the high computational cost of resolving thin diffuse interfaces over the entire domain. This paper presents an efficient strong-form phase-field solver that couples the Gradient Smoothing Method (GSM) with a hierarchical adaptive and moving structured mesh, enabling automatic localization of resolution within a narrow interfacial region while retaining coarse discretization in bulk domains. A layered refinement design is introduced to preserve locally uniform resolution across the interface, allowing the GSM discretization to maintain overall second-order accuracy despite strong mesh non-uniformity away from the interface. Although GSM incurs a higher per-degree-of-freedom cost than standard finite-difference schemes, the adaptive framework substantially reduces the total number of degrees of freedom, resulting in near-linear computational scaling compared with the quadratic scaling of uniform-grid approaches. Numerical examples based on the Allen-Cahn and Cahn-Hilliard equations demonstrate that the proposed adaptive GSM solver delivers desired accuracy for interface evolution while attaining more favorable computational complexity, O(N), than existing weak-form and strong-form solvers, becoming significantly more efficient for large-scale problems with thin interfaces or a small interfacial area fraction relative to the whole domain.

math.NA↗

arXiv2026

Spectral Principal Paths: A Spectral Perspective on Linear Representation Formation in LLMs

High-level representations have become a central focus in enhancing AI transparency and control, shifting attention from individual neurons or circuits to structured semantic directions that align with human-interpretable concepts. While the Linear Representation Hypothesis (LRH) suggests that such directions emerge in representations, it remains unclear how these representations originate and why they become increasingly stable across layers. To solve this issue, we introduce the Input-Space Linearity Hypothesis, positing that concept-aligned directions originate in the input space and are steadily maintained with increasing depth. We then propose the Spectral Principal Path (SPP) framework, which formalizes how deep networks progressively distill linear representations along the spectral principal directions. We provide rigorous stability guarantees for the SPP based on the Wedin $\sinΘ$ perturbation theorem, identifying testable conditions, including spectral gap and context incoherence, that jointly ensure layer-wise directional preservation. By bridging theoretical analysis with empirical evidence, this work identifies a spectral view of how linear representations arise in LLMs, and suggests potential implications for concept-level controllable, robust, and coherent approaches to fairness and transparency in modern AI systems.

cs.CV↗

arXiv2026

Characteristic Polynomials of Graph- and Digraph-Deleted Catalan Arrangements

We develop a finite-field stratification for characteristic polynomials of deletion subarrangements of the full $m$-Catalan arrangement. It reduces the count to cyclic placements of rigid blocks and yields falling-factorial expansions for deletions indexed by graphs, digraphs, and gain-labeled digraphs. The coefficients are graphical Stirling numbers for zero-layer deletions, directed matching numbers when the deleted layer $\ell$ satisfies $1\le \ell\le\lfloor m/2\rfloor$, directed path-cover numbers when $\lfloor m/2\rfloor<\ell\le m$, and admissible gain-labeled arc sets for multilayer deletions. For $\ell=m$, a complementary path-cover expansion yields factorization consequences. The method also gives formulas for directed Ish-type arrangements in terms of path-cycle covers and outdegrees.

math.CO↗

arXiv2026

PriEco-DRL: Joint Optimization of Electric-Bus Eco-Driving and Transit-Priority Adaptive Signals via Deep Reinforcement Learning

Urban transit electrification requires balancing energy efficiency, schedule reliability, and ride comfort for electric buses (EBs), particularly when interacting with transit-priority adaptive signals in congested networks. This paper proposes PriEco-DRL, a joint optimization framework that integrates EB eco-driving with transit-priority adaptive signal control using deep reinforcement learning (DRL). The signal layer employs a priority-weighted max-pressure (Priority-MP) controller to allocate green time based on occupancy-aware pressures, while the vehicle layer adapts longitudinal control based on uncertain and dynamically evolving local signal cues. A structured reward combines guidance and event-based reinforcement to align EB arrivals with green opportunities while considering energy, time, comfort, and safety. The framework uses centralized training and decentralized execution (CTDE) with parameter sharing, allowing a single DRL agent to learn from multiple buses and routes using local observations. Experiments on a real-world corridor show that PriEco-DRL reduces EB energy consumption while maintaining network efficiency and transit priority compared with fixed-time, actuated, and rule-based signal-vehicle coordination baselines. Energy- and trajectory-based analyses reveal that the improvements stem from fewer unscheduled stop-start events and smoother speed regulation under adaptive signals. The results highlight a tunable energy-time trade-off, allowing flexible operational choices through reward weighting.

math.OC↗

arXiv2026

Research on Cross-media Science and Technology Information Data Retrieval

Since the era of big data, the Internet has been flooded with all kinds of information. Browsing information through the Internet has become an integral part of people's daily life. Unlike news data and social data on the Internet, cross-media science and technology information data has different characteristics. This data has become an important basis for researchers and scholars to track current hot spots and explore future directions of technology development. As the volume of science and technology information data becomes richer, traditional science and technology information retrieval systems, which support only unimodal data retrieval and use outdated keyword-matching models, can no longer meet the daily retrieval needs of science and technology scholars. Therefore, in view of this research background, it is of profound practical significance to study cross-media science and technology information data retrieval systems based on deep semantic features, in line with domestic and international technology-development trends.

cs.IR↗

arXiv2026

Research on Intellectual Property Resource Profile and Evolution Law

In the era of big data, intellectual property-oriented scientific and technological resources show the trend of large data scale, high information density, and low value density, which brings severe challenges to the effective use of intellectual property resources, and the demand for mining hidden information in intellectual property is increasing. This makes intellectual property-oriented science and technology resource portraits and analysis of evolution become a current research hotspot. This paper sorts out the construction method of intellectual property resource portraits and its preliminary work, including property entity extraction and entity completion, from the aspects of algorithm classification and general process, and identifies directions for improving future methods.

cs.DL↗